{ "cells": [ { "cell_type": "markdown", "id": "8e7ea252-0a88-4c14-9197-8ad15eb14751", "metadata": {}, "source": [ "# Running alchemical free energy calculations\n", "\n", "`ASAP-Alchemy` provides a set of automated workflows which when combined create and end-to-end pipeline enabling the routine running of state-of-the-art alchemical free energy calculations at (Alchemi)scale! In this tutorial we cover configuring and executing each of the workflows via the python API, but note `ASAP-Alchemy` also provides a CLI which can be used to execute any of the workflows and accepts custom configuration files providing the same amount of flexibility as the API but with easier execution! We also have tried and tested defaults in all of our workflows which are used in production so feel free to skip configuring each workflow unless you really need the extra flexibility.\n", "\n", "## General design\n", "Each of the workflows in `ASAP-Alchemy` are designed following the same factory pattern which allows them to have a similar API which should make them all feel familiar. Each workflow then begins as a resuable configuration object which defines the runtime options of that pipeline which can then be applied to sets of molecules.\n", "\n", "## ASAP-Alchemy Prep\n", "The first stage in the workflow is called `prep` and has three main jobs:\n", "\n", "- **State Enumeration**: Enumerate the tautomers, protomers and stereoisomers of the input ligands\n", "\n", "- **Constrained Pose Generation**: Generate initial poses for the lignads while constraining the ligand to match the crystal structure reference conformation.\n", "\n", "- **Partial Charge Generation**: Generate partial atomic charges for the ligands to use in the alchemical free energy calculations.\n", "\n", "We will now walk through the process building a standard `AlchemyPrepWorkflow` and assigning the required component parts:" ] }, { "cell_type": "code", "execution_count": 2, "id": "b5187217-4aed-4437-af13-44a0263dae82", "metadata": {}, "outputs": [], "source": [ "from drugforge.alchemy.schema.prep_workflow import AlchemyPrepWorkflow\n", "from drugforge.alchemy.schema.charge import OpenFFCharges\n", "from drugforge.data.operators.state_expanders.protomer_expander import EpikExpander\n", "from drugforge.data.operators.state_expanders.stereo_expander import StereoExpander\n", "from drugforge.docking.schema.pose_generation import (\n", " OpenEyeConstrainedPoseGenerator,\n", " RDKitConstrainedPoseGenerator,\n", ")\n", "\n", "prep_workflow = AlchemyPrepWorkflow()" ] }, { "cell_type": "markdown", "id": "c112525b-b097-49aa-8710-47906cf8659a", "metadata": {}, "source": [ "## Stereo expansion\n", "\n", "Molecules with unknown stereo centers should be fully expanded before generating an initial pose to ensure we know the exact identity of the molecule we are making the prediction for, this also allows us to predict free energy differeces between steroisomers which might offer more insight to your medicinal chemistry team. \n", "\n", "So lets add our `openeye` stereo expander module to the workflow and set it to only expand any undefined stereo centers in the input molecules:" ] }, { "cell_type": "code", "execution_count": 3, "id": "8bbb34bf", "metadata": {}, "outputs": [], "source": [ "stereo_expander = StereoExpander(stereo_expand_defined=False)\n", "prep_workflow.stereo_expander = stereo_expander" ] }, { "cell_type": "markdown", "id": "95460c85", "metadata": {}, "source": [ "## Charge and Tautomeric expansion\n", "\n", "Druglike molecules often have multipule accessible protonation and tautomeric states at experimental pH which can contribute to binding and considering only a single state in alchemical free energy calculations can introduce significiant error. One option is to use tools like OpenEye to try and predict the most reasonable form of a molecule at the experimental pH in question, another is to enumerate all possible forms and and use a state pentalty correction scheme based on the predicted pKa of each state. You can read more about this at .\n", "\n", "`ASAP-Alchemy` prodvies both of these enumeration options (`EpikExpander`, `ProtomerExpander`, `TautomerExpander`) however they are under active development and so by default we skip this stage by setting it to `None`:" ] }, { "cell_type": "code", "execution_count": 4, "id": "15497ee8", "metadata": {}, "outputs": [], "source": [ "prep_workflow.charge_expander = None" ] }, { "cell_type": "markdown", "id": "412b2a14", "metadata": {}, "source": [ "## Pose Generator\n", "\n", "We now need to select a backend which will be used to generate the initial poses for our molecules. We have two options available `OpenEyeConstrainedPoseGenerator` and the `RDKitConstrainedPoseGenerator`. Both implimentations use the same general workflow to generate the poses, that is: find the MCS overlap between the target ligand and some reference ligand (normally extracted from a crystal reference structure) and constrain the overlaping atoms of the target ligand to match the reference; then, for any atoms not constrained, enumerate the rotamers of any rotable bonds using the backend toolkit and filter down to a single favorable conformer for each target ligand in the series. \n", "\n", "By default we use the `RDKitConstrainedPoseGenerator` as follows:" ] }, { "cell_type": "code", "execution_count": 5, "id": "45e97fc9", "metadata": {}, "outputs": [], "source": [ "pose_generator = RDKitConstrainedPoseGenerator(\n", " max_confs = 300, # The maximum number of conformers to try and generate\n", " rms_thresh = 0.2, # The RMSD between the heavy atoms which should be used to filter duplicated conformers\n", " mcs_timeout = 1, # The maximum time in seconds to the MCS search for \n", " clash_cutoff = 2.0, # The distance cutoff for which we check for clashes between poses and the receptor in Angstroms\n", " selector = 'Chemgauss3', # The method which should be used to select the best conformer, an openeye docking score in this case\n", " backup_score = 'Sage', # If the main scoring function fails the intramolecular energies calculated with this force field will be used to select the best conformer\n", ")\n", "prep_workflow.pose_generator = pose_generator" ] }, { "cell_type": "markdown", "id": "4add574a", "metadata": {}, "source": [ "## Core Smarts \n", "\n", "In some cases you may want to define the core yourself, or you may not want to constrain the full MCS between a target molecule and the reference ligand, in these cases you can provide a core SMARTS pattern which will be used to define the MCS. While [SMARTS](https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html) patterns can be created by hand we recomend using ChemDraw or . By default we let the pose generator find the MCS which can be done by setting the `core_smarts` field to `None`." ] }, { "cell_type": "code", "execution_count": 6, "id": "c7f0c095", "metadata": {}, "outputs": [], "source": [ "prep_workflow.core_smarts = None" ] }, { "cell_type": "markdown", "id": "66f3aa59", "metadata": {}, "source": [ "## Stereochemistry filtering\n", "\n", "During the pose generation process some molecules may end up with inconsistent stereochemistry, that is the stereochemistry we intended does not match the 3D geometry of the molecule. This often happens when our reference ligand contains a stereocenter and the target ligand we are trying to pose has opposing stereochemsitry, in these cases a sensible pose can not be generated and often requires manual modeling of the reference to allow opposing stereocenters to be generated. To ensure all molecules have the correct stereo chemistry we set the `strict_stereo` flag to `True`:" ] }, { "cell_type": "code", "execution_count": 7, "id": "982e8d8d", "metadata": {}, "outputs": [], "source": [ "prep_workflow.strict_stereo = True" ] }, { "cell_type": "markdown", "id": "35d0b8c2", "metadata": {}, "source": [ "## Experimental references\n", "\n", "In prospective predictions it is desirable to not only predict a ranking of the molecules in the series but to predict the absolute binding affinity so that the results might be compared accross alchemical networks and against other ligands with experimental data which are not included in the network. \n", "\n", "In practice this is done by including experimental reference compounds in the alchemical network and then shifting the final predictions by the mean of the experimantal absolute binding afinities. `ASAP-Alchemy` allows users to provide a list of ligands with experimental data as references which we then try to generate poses for. From this list, `ASAP-Alchemy` will extract `n` suitable ligands to include in your network. \n", "\n", "The number of references to include from that list can be controlled via the `n_references` field which by default is set to 3:" ] }, { "cell_type": "code", "execution_count": 8, "id": "e6edcf3e", "metadata": {}, "outputs": [], "source": [ "prep_workflow.n_references = 3" ] }, { "cell_type": "markdown", "id": "f2d825ca", "metadata": {}, "source": [ "## Partial Charge Generation\n", "\n", "Unlike all of the other terms in a molecular mechanics force field the partial charges are usually generated on the fly using a semi-emprical quantum mechanics based method such as `am1bcc` which is a fast approximation for higher theory charges derived from a restrained fit to the electrostatic potential (RESP) calculated using HF/6-31G*. To ensure reproducability of our calculations we recomend assigning charges locally before simulation instead of during system creation, this also acts as a source of provenance for the charges and the assignment method which is often lost. \n", "\n", "By default we use the `OpenFFCharges` generator which can generate two types of charges `am1bccelf10` or `am1bcc`:" ] }, { "cell_type": "code", "execution_count": 9, "id": "910762d7", "metadata": {}, "outputs": [], "source": [ "charge_method = OpenFFCharges(charge_method=\"am1bccelf10\")\n", "prep_workflow.charge_method = charge_method" ] }, { "cell_type": "markdown", "id": "43d28ad9", "metadata": {}, "source": [ "This completes the construction of the default `AlchemyPrepWorkflow`, and we can now view the settings of the workflow" ] }, { "cell_type": "code", "execution_count": 10, "id": "a260377f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AlchemyPrepWorkflow(type='AlchemyPrepWorkflow', stereo_expander=StereoExpander(expander_type='StereoExpander', stereo_expand_defined=False), charge_expander=None, pose_generator=RDKitConstrainedPoseGenerator(type='RDKitConstrainedPoseGenerator', clash_cutoff=2.0, selector=, backup_score=, max_confs=300, rms_thresh=0.2, mcs_timeout=1), core_smarts=None, strict_stereo=True, n_references=3, charge_method=OpenFFCharges(type='OpenFFCharges', charge_method='am1bccelf10'))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prep_workflow" ] }, { "cell_type": "markdown", "id": "3ab444d3", "metadata": {}, "source": [ "## Saving and Loading\n", "\n", "At this point, configured workflows can be saved and loaded to `JSON` meaning that a workflow can be reused multipule times throughout a project to ensure that a consistent pipeline is applied to an entire series of alchemical free energy calculations in a given project." ] }, { "cell_type": "code", "execution_count": 11, "id": "b5c83ac1", "metadata": {}, "outputs": [], "source": [ "prep_workflow.to_file(filename=\"My-prep-workflow.json\")\n", "prep_workflow_2 = AlchemyPrepWorkflow.from_file(\"My-prep-workflow.json\")" ] }, { "cell_type": "markdown", "id": "832e493f", "metadata": {}, "source": [ "## Running Alchemy prep\n", "\n", "We are now ready to run our configured prep workflow and create an alchemy dataset which can be used in the next sections of the guide. Lets inspect the function to create the dataset and generate the missing parts:" ] }, { "cell_type": "code", "execution_count": 12, "id": "074322e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0;31mSignature:\u001b[0m\n", "\u001b[0mprep_workflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_alchemy_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdataset_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mligands\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0masapdiscovery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mligand\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLigand\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mreference_complex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0masapdiscovery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPreppedComplex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mprocessors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mreference_ligands\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0masapdiscovery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mligand\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLigand\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0masapdiscovery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malchemy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprep_workflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAlchemyDataSet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Run the set of input ligands through the state enumeration and pose generation workflow to create a set of posed\n", "ligands ready for ASAP-Alchemy.\n", "\n", "Notes:\n", " Ligands with experimental data can be supplied via `reference_ligands`, poses will be generated\n", " until `self.n_references` have been successfully added. The ligands will be sorted by their MCS overlap with\n", " the crystal reference ligand to ensure a pose can be generated.\n", "\n", "Args:\n", " dataset_name: The name which should be given to this dataset.\n", " ligands: The list of input ligands which should be run through the workflow.\n", " reference_complex: The prepared target crystal structure with a reference ligand which the poses should be\n", " constrained to.\n", " processors: The number of parallel processors that should be used to run the workflow.\n", " reference_ligands: The list of reference ligands with experimental data which we should also generate\n", " poses for if `self.n_references` > 0.\n", "\n", "Returns:\n", " A prepared AlchemyDataset with state expanded ligands posed in the receptor ready for FEC, along with the\n", " provenance information of the workflow.\n", "\u001b[0;31mFile:\u001b[0m ~/Documents/Software/asapdiscovery/asapdiscovery-alchemy/asapdiscovery/alchemy/schema/prep_workflow.py\n", "\u001b[0;31mType:\u001b[0m method" ] } ], "source": [ "prep_workflow.create_alchemy_dataset?" ] }, { "cell_type": "markdown", "id": "224929dd", "metadata": {}, "source": [ "First grab a reference complex, in this example we will use a real ASAP-enabled target ([SARS-CoV-2 NSP3 macrodomain](https://asapdiscovery.org/outputs/molecules/#ASAP-SARS-COV-2-NSP3-MAC1)) and download a prepared complex from the asapdiscovery test suite which includes a prepared receptor and ligand." ] }, { "cell_type": "code", "execution_count": 13, "id": "52ca71dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PreppedComplex(target=PreppedTarget(target_name='SARS2_Mac1A_A1496-ASAP-0008674-001', ids=None, data_format=, target_hash='22bf5e21b05a8a454b5b64c52bcd95db83088d72636ee37cf37f166d71708331'), ligand=Ligand(compound_name='SARS2_Mac1A_A1496-ASAP-0008674-001_ligand', ids=None, provenance=LigandProvenance(isomeric_smiles='CC(C)[C@@H](c1ccc2c(c1)S(=O)(=O)CCC2)Nc3c4cc[nH]c4ncn3', inchi='InChI=1S/C19H22N4O2S/c1-12(2)17(23-19-15-7-8-20-18(15)21-11-22-19)14-6-5-13-4-3-9-26(24,25)16(13)10-14/h5-8,10-12,17H,3-4,9H2,1-2H3,(H2,20,21,22,23)/t17-/m0/s1', inchi_key='WLJITGAGZLIWOY-KRWDZBQOSA-N', fixed_inchi='InChI=1/C19H22N4O2S/c1-12(2)17(23-19-15-7-8-20-18(15)21-11-22-19)14-6-5-13-4-3-9-26(24,25)16(13)10-14/h5-8,10-12,17H,3-4,9H2,1-2H3,(H2,20,21,22,23)/t17-/m0/s1/f/h20,23H', fixed_inchikey='WLJITGAGZLIWOY-ZFFWTUEDNA-N'), experimental_data=None, expansion_tag=None, charge_provenance=None, tags={}, conf_tags={}, data_format=))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from drugforge.data.testing.test_resources import fetch_test_file\n", "from drugforge.data.schema.complex import PreppedComplex\n", "\n", "mac1_complex = PreppedComplex.parse_file(fetch_test_file(\"constrained_conformer/complex.json\"))\n", "mac1_complex\n" ] }, { "cell_type": "markdown", "id": "d8cd9368", "metadata": {}, "source": [ "We now need a list of target molecules for which we want to estimate the binding afinity via relative free energy calculations, in this case we'll define some very simple molecules as an example:" ] }, { "cell_type": "code", "execution_count": 14, "id": "0521d1d8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from drugforge.data.schema.ligand import Ligand\n", "from rdkit.Chem import Draw\n", "\n", "molecules_smiles = [\n", " \"CC(C)[C@H](Nc1ncnc2[nH]c(Cl)cc12)c4ccc3CCCS(=O)(=O)c3c4\",\n", " \"CC(C)[C@H](Nc1ncnc2[nH]c(F)cc12)c4ccc3CCCS(=O)(=O)c3c4\",\n", " \"CC(C)[C@H](Nc1ncnc2[nH]c(O)cc12)c4ccc3CCCS(=O)(=O)c3c4\"\n", "]\n", "target_ligands = [\n", " Ligand.from_smiles(smiles=smiles, compound_name=f\"ASAP-MAC1-{i}\")\n", " for i, smiles in enumerate(molecules_smiles)\n", "]\n", "Draw.MolsToGridImage(mols=[mol.to_rdkit() for mol in target_ligands], legends=[mol.compound_name for mol in target_ligands])" ] }, { "cell_type": "markdown", "id": "6278417f", "metadata": {}, "source": [ "Finally we can optionally pass in a set of reference ligands which can be added to the dataset to ensure the absolute predictions of the binding affinities are comparable with other experimental results. These ligands can be provided from any source so long as they are marked as experimental, we also provide tools to extract reference ligands from a [CDD vault](https://www.collaborativedrug.com/). Here we will make some example reference ligands:" ] }, { "cell_type": "code", "execution_count": 15, "id": "adc4a85c", "metadata": {}, "outputs": [ { "data": { "image/png": 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In production it is recommended to use around 300 with RDKit." ] }, { "cell_type": "code", "execution_count": 16, "id": "f6a05db4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5fbad3e6d7ec4c5facfad06f529f4527", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[✓] StereoExpander successful,  number of unique ligands 3.\n",
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[✓] Pose generation successful for 3/3.\n",
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[✓] Stereochemistry filtering complete 0 molecules removed.\n",
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[✓] Pose generation successful for 3/3 experimental ligands.\n",
       "
\n" ], "text/plain": [ "\u001b[1m[\u001b[0m\u001b[32m✓\u001b[0m\u001b[1m]\u001b[0m Pose generation successful for \u001b[1;36m3\u001b[0m/\u001b[1;36m3\u001b[0m experimental ligands.\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
                                                                                                                   \n",
       "Poses successfully generated for 6 ligands.                                                                        \n",
       "                                                                                                                   \n",
       "
\n" ], "text/plain": [ " \n", "Poses successfully generated for \u001b[1;36m6\u001b[0m ligands. \n", " \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1f1e9efc62e04b2e933b50a315e7a4d4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
                                                                                                                   \n",
       "[✓] Charges successfully generated.                                                                                \n",
       "                                                                                                                   \n",
       "
\n" ], "text/plain": [ " \n", "\u001b[1m[\u001b[0m\u001b[32m✓\u001b[0m\u001b[1m]\u001b[0m Charges successfully generated. \n", " \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# reduce the number of conformers just for this example\n", "prep_workflow.pose_generator.max_confs = 10\n", "alchemy_dataset = prep_workflow.create_alchemy_dataset(\n", " dataset_name=\"my-first-asap-dataset\",\n", " ligands=target_ligands,\n", " reference_complex=mac1_complex,\n", " reference_ligands=reference_ligands,\n", ")" ] }, { "cell_type": "markdown", "id": "fcb4942a", "metadata": {}, "source": [ "We now have an alchemy dataset object which contains information about the workflow we have just ran including the inputs and any errors that might cause poses to not be generated for some ligands. We can also inspect provenance information about the stages run including the versions of software. For example, let's look at the pose generator and the software versions used:" ] }, { "cell_type": "code", "execution_count": 17, "id": "9176ecd3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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      "text/plain": []
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     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/plain": [
       "\n",
       "\u001b[1;35mRDKitConstrainedPoseGenerator\u001b[0m\u001b[1m(\u001b[0m\n",
       "    \u001b[33mtype\u001b[0m=\u001b[32m'RDKitConstrainedPoseGenerator'\u001b[0m,\n",
       "    \u001b[33mclash_cutoff\u001b[0m=\u001b[1;36m2\u001b[0m\u001b[1;36m.0\u001b[0m,\n",
       "    \u001b[33mselector\u001b[0m=\u001b[1m<\u001b[0m\u001b[1;95mPoseSelectionMethod.Chemgauss3:\u001b[0m\u001b[39m \u001b[0m\u001b[32m'Chemgauss3'\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33mbackup_score\u001b[0m\u001b[39m=\u001b[0m,\n",
       "    \u001b[33mmax_confs\u001b[0m=\u001b[1;36m10\u001b[0m,\n",
       "    \u001b[33mrms_thresh\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.2\u001b[0m,\n",
       "    \u001b[33mmcs_timeout\u001b[0m=\u001b[1;36m1\u001b[0m\n",
       "\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alchemy_dataset.pose_generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5283db0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'oechem'\u001b[0m: \u001b[1;36m20230910\u001b[0m,\n",
       "    \u001b[32m'oeff'\u001b[0m: \u001b[1;36m20230910\u001b[0m,\n",
       "    \u001b[32m'oedocking'\u001b[0m: \u001b[1;36m20230910\u001b[0m,\n",
       "    \u001b[32m'rdkit'\u001b[0m: \u001b[32m'2023.09.4'\u001b[0m,\n",
       "    \u001b[32m'openff.toolkit'\u001b[0m: \u001b[32m'0.14.3'\u001b[0m\n",
       "\u001b[1m}\u001b[0m"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alchemy_dataset.provenance[\"RDKitConstrainedPoseGenerator\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77c09fc",
   "metadata": {},
   "source": [
    "We can also inspect the posed ligands and write them to file to view them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "597dc6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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       "    \u001b[1m}\u001b[0m,\n",
       "    \u001b[33mconf_tags\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'Chemgauss3_score'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'-65.13711547851562'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m,\n",
       "    \u001b[33mdata_format\u001b[0m=\u001b[1m<\u001b[0m\u001b[1;95mDataStorageType.sdf:\u001b[0m\u001b[39m \u001b[0m\u001b[32m'sdf'\u001b[0m\u001b[1m>\u001b[0m\n",
       "\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alchemy_dataset.save_posed_ligands(filename='posed_ligands.sdf')\n",
    "alchemy_dataset.posed_ligands[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d646afb",
   "metadata": {},
   "source": [
    "We might want to view the ligands in the receptor and overlay the reference structure, so now we will write the reference receptor to a local PDB file and the ligand to an SDF file. You can then use your any molecule viewer (like PyMOL) or use the example below which uses `py3dmol`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92bfb320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ "
\n", "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import py3Dmol\n", "alchemy_dataset.reference_complex.target.to_pdb_file('mac1-receptor.pdb')\n", "alchemy_dataset.reference_complex.ligand.to_sdf('mac1-ref-ligand.sdf')\n", "view = py3Dmol.view(width=400, height=300)\n", "view.addModel(open('mac1-receptor.pdb').read(), 'pdb')\n", "view.setStyle({'chain':['A', 'B']}, {'cartoon': {'color': 'spectrum'}})\n", "view.addModel(alchemy_dataset.reference_complex.ligand.to_sdf_str())\n", "for mol in alchemy_dataset.posed_ligands:\n", " view.addModel(mol.to_sdf_str())\n", "view.setStyle({'model': [i + 1 for i in range(len(alchemy_dataset.posed_ligands))]}, {\"stick\":{}})\n", "view.zoomTo({'model':1})\n", "view.show()" ] }, { "cell_type": "markdown", "id": "29c163b9", "metadata": {}, "source": [ "We can also inspect the charges generated for the molecules visually to ensure they make sense, here we will use a similartiy map from rdkit where positive charges are shown in green and negative in red." ] }, { "cell_type": "code", "execution_count": 21, "id": "8819ff19", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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     "metadata": {},
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    {
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       ""
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.SVG\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "from IPython.display import SVG\n",
    "from rdkit.Chem.Draw import SimilarityMaps\n",
    "rdkit_mol = alchemy_dataset.posed_ligands[0].to_rdkit()\n",
    "AllChem.Compute2DCoords(rdkit_mol)\n",
    "charges = [a.GetDoubleProp('PartialCharge') for a in rdkit_mol.GetAtoms()]\n",
    "d2d = Draw.rdMolDraw2D.MolDraw2DSVG(600, 400)\n",
    "SimilarityMaps.GetSimilarityMapFromWeights(rdkit_mol, charges, draw2d=d2d)\n",
    "d2d.FinishDrawing()\n",
    "SVG(d2d.GetDrawingText())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b380a3",
   "metadata": {},
   "source": [
    "This dataset object can then be saved to file and acts as a form of provenance for the `prep` workflow and should contain all of the information required to reproduce the dataset should someone want to repeate your work, i.e. all settings, versioning, ligand and protein structures, et cetera."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ecb5db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "alchemy_dataset.to_file(\"my-alchemy-dataset.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb5faeb",
   "metadata": {},
   "source": [
    "## ASAP-Alchemy Plan\n",
    "\n",
    "We are now ready to plan an alchemical free energy network using a state-of-the-art workflow based on [OpenFE](https://docs.openfree.energy/en/stable/) infastructure. The free energy calculations can also be configured for local or distributed execution using [Alchemiscale](https://github.com/openforcefield/alchemiscale). Following the format in the `prep` pipeline we start with an `FreeEnergyCalculationFactory` which we will configure component by component before appling it to our `alchemy_dataset` preppared above. \n",
    "\n",
    "This workflow handles the following stages:\n",
    "- **Network Planning**: Choosing the optimal transformations between ligands based on some atom mapping and scoring metrics\n",
    "\n",
    "- **Protocol Settings**: Defining the run time settings of the resulting network ready for execution using OpenFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "144ae922",
   "metadata": {},
   "outputs": [],
   "source": [
    "from drugforge.alchemy.schema.fec import FreeEnergyCalculationFactory\n",
    "from drugforge.alchemy.schema.network import (\n",
    "    NetworkPlanner, \n",
    "    KartografAtomMapper, \n",
    "    LomapAtomMapper, \n",
    "    PersesAtomMapper, \n",
    "    RadialPlanner,\n",
    "    MaximalPlanner, \n",
    "    MinimalRedundantPlanner, \n",
    "    MinimalSpanningPlanner\n",
    ")\n",
    "alchemy_factory = FreeEnergyCalculationFactory()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10a8bde",
   "metadata": {},
   "source": [
    "## Network Planner\n",
    "\n",
    "The first component of the `FreeEnergyCalculationFactory` is the the `NetworkPlanner` module which configures how the optimal transformations should be selected using OpenFE tooling and can be constructed via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1a5e89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_planner = NetworkPlanner()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6093ff",
   "metadata": {},
   "source": [
    "### Atom Mapping Engine\n",
    "\n",
    "Our free energy calculations by default use a hybrid topology approach and so an atom mapping which identifies the atoms of ligand A which should be alchemicaly transformed to atoms of ligand B is needed. Atoms that are not mapped should be consistend between ligands A/B. We currently support all of the available OpenFE atom mappers (LomapAtomMapper, PersesAtomMapper & KartografAtomMapper) but use Lomap by default which can be constructed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c418059",
   "metadata": {},
   "outputs": [],
   "source": [
    "atom_mapper = LomapAtomMapper(\n",
    "    timeout = 20, # The timeout in seconds of the MCS algorithm in rdkit\n",
    "    threed = True, # If spatial information should be used to choose between symmetrically equivalent mappings \n",
    "    max3d = 1000, # Maximum discrepancy in Angstroms between atoms before the mapping is not allowed\n",
    "    element_change = True, # Whether to allow element changes in the mappings\n",
    "    seed = '', # An optional seed SMARTS string to speed up the MCS, if left blank this is automatically generated\n",
    "    shift = True, # When determining 3D overlap translate the molecules to minimse the RMSD during alignment\n",
    ")\n",
    "network_planner.atom_mapping_engine = atom_mapper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d2ade3",
   "metadata": {},
   "source": [
    "### Transformation Scorer\n",
    "\n",
    "Given the proposed atom mapping (which is generated between every possible combination of ligands in the target set) we need to choose the best edges to run during this campaign. To this end, OpenFE provides scoring metrics which rank the proposed atom mappings; our default is to use the lomap scorer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e6b1530",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_planner.scorer = \"default_lomap\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876321a4",
   "metadata": {},
   "source": [
    "### Network Planning\n",
    "\n",
    "Once we have all of the possible edges scored we then need to pick a strategy to build the network which will determine how many connections it has. The optimal network should provide a balance between speed (not too many edges), accuracy and redundancy. OpenFE provides many basic planning methods (`RadialPlanner`, `MaximalPlanner`, `MinimalSpanningPlanner`, `MinimalRedundantPlanner`) with more under active development. For now, our default is to use the `MinimalRedundantPlanner` which builds a minimal spanning tree ensuring all ligands are connected to the network but also adds `n` extra redundant edge(s) per node which ensures each node is in at least one cycle if `n`=1. `n` can be controlled by the user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "79078af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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     "metadata": {},
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     "data": {
      "text/plain": [
       "\n",
       "\u001b[1;35mNetworkPlanner\u001b[0m\u001b[1m(\u001b[0m\n",
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       "        \u001b[33mthreed\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
       "        \u001b[33mmax3d\u001b[0m=\u001b[1;36m1000\u001b[0m\u001b[1;36m.0\u001b[0m,\n",
       "        \u001b[33melement_change\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
       "        \u001b[33mseed\u001b[0m=\u001b[32m''\u001b[0m,\n",
       "        \u001b[33mshift\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "    \u001b[1m)\u001b[0m,\n",
       "    \u001b[33mscorer\u001b[0m=\u001b[32m'default_lomap'\u001b[0m,\n",
       "    \u001b[33mnetwork_planning_method\u001b[0m=\u001b[1;35mMinimalRedundantPlanner\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'MinimalRedundantPlanner'\u001b[0m, \u001b[33mredundancy\u001b[0m=\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planning_method = MinimalRedundantPlanner(\n",
    "    redundancy = 2\n",
    ")\n",
    "network_planner.network_planning_method = planning_method\n",
    "network_planner"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fbd8b05",
   "metadata": {},
   "source": [
    "The planner can then be saved to file like all `ASAP-Alchemy` workflows and reused throughout a discovery project or combined into our `FreeEnergyCalculationFactory`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "98805705",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_planner.to_file(filename='my-network-planner.json')\n",
    "alchemy_factory.network_planner = network_planner"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ce91abb",
   "metadata": {},
   "source": [
    "## Alchemy Protocol\n",
    "\n",
    "We can now define the extensive set of runtime settings which will be used in the alchemical free energy calculations starting with the OpenFE protocol, so far only one is supported which is the `RelativeHybridTopologyProtocol` and all of the settings are directly related to this protocol. We plan on adding support for other types of calculations in the future so check back soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f4b1e8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "alchemy_factory.protocol = 'RelativeHybridTopologyProtocol'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0852897",
   "metadata": {},
   "source": [
    "### Solvent Settings\n",
    "\n",
    "To accurately represent the experimental conditions our simulations will be performed in explicit solvent, and we begin by defining the settings of the solvent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "673b0448",
   "metadata": {},
   "outputs": [],
   "source": [
    "from drugforge.alchemy.schema.fec import SolventSettings\n",
    "from openff.units import unit\n",
    "solvent = SolventSettings(\n",
    "    smiles = \"O\", # The smiles pattern of the solvent\n",
    "    positive_ion = \"Na+\", # The positive monoatomic ion which should be used to neutralize the system\n",
    "    negative_ion = \"Cl-\", # The negative monoatomic ion which should be used to neutralize the system\n",
    "    neutralize = True, # If we should add ions to neutralize the total charge of the system\n",
    "    ion_concentration = 0.15 * unit.molar, # The ionic concentration required in molar units\n",
    ")\n",
    "alchemy_factory.solvent_settings = solvent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b87f5ea",
   "metadata": {},
   "source": [
    "### Force Field Settings\n",
    "\n",
    "The protocol also gives us control over the force fields used to parameterize all of the components of the system including the small molecule force field which allows for bespoke parameters (bespoke parameter implementation avaible on [Quaid-Alchemy](https://github.com/QuaidAlchemy/quaid)). For now, we use the standard default settings but ensure that we always use the most recent OpenFF force field which at the time of writing is openff-2.1.0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7555fe78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gufe import settings\n",
    "ff_settings = settings.OpenMMSystemGeneratorFFSettings(\n",
    "    small_molecule_forcefield=\"openff-2.1.0\"\n",
    ")\n",
    "alchemy_factory.forcefield_settings = ff_settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5a2217",
   "metadata": {},
   "source": [
    "### Thermodynamic Settings \n",
    "\n",
    "We can explicitly set the temperature and pressure of the simulation to ensure we match the experimental conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2d13bbbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "thermo_settings = settings.ThermoSettings(\n",
    "    temperature = 298.15 * unit.kelvin,\n",
    "    pressure = 1 * unit.bar\n",
    ")\n",
    "alchemy_factory.thermo_settings = thermo_settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c63b7e",
   "metadata": {},
   "source": [
    "
\n", "Warning: The next few sections offer advanced control over the simulation settings specific to OpenMM and are normally best left to their defaults!\n", "
" ] }, { "cell_type": "markdown", "id": "bd819758", "metadata": {}, "source": [ "### OpenMM System Settings\n", "\n", "This allows to change the non-bonded settings in our simulation such as the method used to calculate the long-range charge interactions and the cutoff for short range non-bonded interactions.\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "c2a179f8", "metadata": {}, "outputs": [], "source": [ "from openfe.protocols.openmm_rfe.equil_rfe_settings import (\n", " AlchemicalSamplerSettings,\n", " AlchemicalSettings,\n", " IntegratorSettings,\n", " OpenMMEngineSettings,\n", " SimulationSettings,\n", " SolvationSettings,\n", " SystemSettings,\n", ")\n", "alchemy_factory.system_settings = SystemSettings()" ] }, { "cell_type": "markdown", "id": "44f87f1c", "metadata": {}, "source": [ "### Solvation Settings\n", "\n", "Not to be confused with solvent settings, the solvation settings control how the solvent will be added to the system, i.e. the water model used (3, 4 or 5 point water) and how much solvent should be added to ensure a minimum distance between the solutes and edge of the surrounding solvent." ] }, { "cell_type": "code", "execution_count": 34, "id": "a8f36fef", "metadata": {}, "outputs": [], "source": [ "alchemy_factory.solvation_settings = SolvationSettings()" ] }, { "cell_type": "markdown", "id": "37dad363", "metadata": {}, "source": [ "### Alchemical Settings\n", "\n", "This controls the used lambda schedule and the creation of the hybrid system:" ] }, { "cell_type": "code", "execution_count": 35, "id": "d3ea1765", "metadata": {}, "outputs": [], "source": [ "alchemy_factory.alchemical_settings = AlchemicalSettings()" ] }, { "cell_type": "markdown", "id": "fc29ba42", "metadata": {}, "source": [ "### Alchemical Sampler Settings\n", "\n", "This defines the equilibrium sampler (ReplicaExchangeSampler, SAMSSampler or MultistateSampler) to use and its run time settings, currently we use `repex` (ReplicaExchangeSampler) by default. The only change we make here is to set the number of repeats to one. This means that each edge is only executed once in a single job and we instead do repeats by doing the calculation multiple times in parallel accross different GPUs via alchemiscale, see the `n_repeats` field on the `FreeEnergyCalculationFactory` class." ] }, { "cell_type": "code", "execution_count": 36, "id": "162ca241", "metadata": {}, "outputs": [], "source": [ "alchemy_factory.alchemical_sampler_settings = AlchemicalSamplerSettings(\n", " n_repeats = 1\n", ")" ] }, { "cell_type": "markdown", "id": "ceb124bc", "metadata": {}, "source": [ "### Engine Settings\n", "\n", "OpenMM specific settings are defined here like the compute platform to use (CPU/GPU), by default we leave this as `None` which allows OpenMM to select the fastest available for us on the hardware that the simulations are run on:" ] }, { "cell_type": "code", "execution_count": 37, "id": "89b7aea8", "metadata": {}, "outputs": [], "source": [ "alchemy_factory.engine_settings = OpenMMEngineSettings()" ] }, { "cell_type": "markdown", "id": "74725fd5", "metadata": {}, "source": [ "### Integrator Settings\n", "\n", "We can also configure the settings used to build the `LangevinSplittingDynamicsMove` integrator in OpenMM such as the timestep or collison rate:" ] }, { "cell_type": "code", "execution_count": 38, "id": "9969b380", "metadata": {}, "outputs": [], "source": [ "alchemy_factory.integrator_settings = IntegratorSettings()" ] }, { "cell_type": "markdown", "id": "1a444fb0", "metadata": {}, "source": [ "### Simulation Settings\n", "\n", "General settings about the simulation length are defined here:" ] }, { "cell_type": "code", "execution_count": 39, "id": "1810b93b", "metadata": {}, "outputs": [], "source": [ "simulation_settings = SimulationSettings(\n", " equilibration_length=1.0 * unit.nanoseconds,\n", " production_length=5.0 * unit.nanoseconds,\n", ")\n", "alchemy_factory.simulation_settings = simulation_settings" ] }, { "cell_type": "markdown", "id": "2ef15bc7", "metadata": {}, "source": [ "### Repeats\n", "\n", "To ensure our estimation of the relative free energy is accurate we repeat each calculation and take the average prediction. We also use the standard deviation across repeats as an estimate of error. In the future this might also allow us to detect possible bad edges due to repeates differing significantly and remove these unreliable edges, although this functionality has not yet been built into `ASAP-Alchemy`. By default we do two repeats of each edge which means its run a total of three times and are submitted as different jobs on alchemiscale to increase throughput:" ] }, { "cell_type": "code", "execution_count": 40, "id": "5b84a0af", "metadata": {}, "outputs": [], "source": [ "alchemy_factory.n_repeats = 2 " ] }, { "cell_type": "markdown", "id": "0a791a21", "metadata": {}, "source": [ "This then completes our `FreeEnergyCalculationFactory` which can now be saved to file and reused over the course of a campaign:" ] }, { "cell_type": "code", "execution_count": 41, "id": "9f23f93b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "        \u001b[33mpositive_ion\u001b[0m=\u001b[32m'Na+'\u001b[0m,\n",
       "        \u001b[33mnegative_ion\u001b[0m=\u001b[32m'Cl-'\u001b[0m,\n",
       "        \u001b[33mneutralize\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
       "        \u001b[33mion_concentration\u001b[0m=\u001b[1m<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'molar'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33mforcefield_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mOpenMMSystemGeneratorFFSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mconstraints\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'hbonds'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mrigid_water\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mremove_com\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mhydrogen_mass\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mforcefields\u001b[0m\u001b[39m=\u001b[0m\u001b[1;39m[\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[32m'amber/ff14SB.xml'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[32m'amber/tip3p_standard.xml'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[32m'amber/tip3p_HFE_multivalent.xml'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[32m'amber/phosaa10.xml'\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m]\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msmall_molecule_forcefield\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'openff-2.1.0'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33mthermo_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mThermoSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mpressure\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.986923267\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'standard_atmosphere'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mph\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mredox_potential\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33msystem_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mSystemSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mnonbonded_method\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'PME'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mnonbonded_cutoff\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'nanometer'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33msolvation_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mSolvationSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msolvent_model\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'tip3p'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msolvent_padding\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m1.2\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'nanometer'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33malchemical_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mAlchemicalSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlambda_functions\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'default'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlambda_windows\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m11\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33munsampled_endstates\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muse_dispersion_correction\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msoftcore_LJ_v2\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msoftcore_alpha\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;36m.85\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33minterpolate_old_and_new_14s\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mflatten_torsions\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33malchemical_sampler_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mAlchemicalSamplerSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33monline_analysis_interval\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m250\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mn_repeats\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msampler_method\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'repex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33monline_analysis_target_error\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'boltzmann_constant * kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33monline_analysis_minimum_iterations\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m500\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mflatness_criteria\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'logZ-flatness'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mgamma0\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;36m.0\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mn_replicas\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m11\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33mengine_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mOpenMMEngineSettings\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mcompute_platform\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33mintegrator_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mIntegratorSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtimestep\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'femtosecond'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcollision_rate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'1 / picosecond'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mn_steps\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m250\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'timestep'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mreassign_velocities\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mn_restart_attempts\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mconstraint_tolerance\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m1e\u001b[0m\u001b[1;36m-06\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mbarostat_frequency\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m25\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'timestep'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33msimulation_settings\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mSimulationSettings\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mequilibration_length\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'nanosecond'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mproduction_length\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m5.0\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'nanosecond'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mforcefield_cache\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'db.json'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mminimization_steps\u001b[0m\u001b[39m=\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33moutput_filename\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'simulation.nc'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33moutput_structure\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'hybrid_system.pdb'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33moutput_indices\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'not water'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcheckpoint_interval\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m250\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'timestep'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[1m>\u001b[0m,\n",
       "        \u001b[33mcheckpoint_storage\u001b[0m=\u001b[32m'checkpoint.nc'\u001b[0m\n",
       "    \u001b[1m)\u001b[0m,\n",
       "    \u001b[33mprotocol\u001b[0m=\u001b[32m'RelativeHybridTopologyProtocol'\u001b[0m,\n",
       "    \u001b[33mn_repeats\u001b[0m=\u001b[1;36m2\u001b[0m,\n",
       "    \u001b[33mnetwork_planner\u001b[0m=\u001b[1;35mNetworkPlanner\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mtype\u001b[0m=\u001b[32m'NetworkPlanner'\u001b[0m,\n",
       "        \u001b[33matom_mapping_engine\u001b[0m=\u001b[1;35mLomapAtomMapper\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mtype\u001b[0m=\u001b[32m'LomapAtomMapper'\u001b[0m,\n",
       "            \u001b[33mtimeout\u001b[0m=\u001b[1;36m20\u001b[0m,\n",
       "            \u001b[33mthreed\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
       "            \u001b[33mmax3d\u001b[0m=\u001b[1;36m1000\u001b[0m\u001b[1;36m.0\u001b[0m,\n",
       "            \u001b[33melement_change\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
       "            \u001b[33mseed\u001b[0m=\u001b[32m''\u001b[0m,\n",
       "            \u001b[33mshift\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mscorer\u001b[0m=\u001b[32m'default_lomap'\u001b[0m,\n",
       "        \u001b[33mnetwork_planning_method\u001b[0m=\u001b[1;35mMinimalRedundantPlanner\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'MinimalRedundantPlanner'\u001b[0m, \u001b[33mredundancy\u001b[0m=\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alchemy_factory.to_file(\"my-alchemy-factory.json\")\n",
    "alchemy_factory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f29c4c8",
   "metadata": {},
   "source": [
    "## The FreeEnergyCalculationNetwork\n",
    "\n",
    "We are now ready to create a `FreeEnergyCalculationNetwork` by applying our `alchemy_factory` to our `alchemy_dataset`. First, let's inspect the function and workout how to feed in our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4323de9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0malchemy_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_fec_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdataset_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mreceptor\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgufe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproteincomponent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProteinComponent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mligands\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ligand'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mcentral_ligand\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mForwardRef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Ligand'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mexperimental_protocol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0masapdiscovery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malchemy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFreeEnergyCalculationNetwork\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      " Use the factory settings to create a FEC dataset using OpenFE models.\n",
      "\n",
      "Args:\n",
      "    dataset_name: The name which should be given to this dataset, this will be used for local file creation or\n",
      "        to identify on alchemiscale\n",
      "    receptor: The prepared receptor to use in the FEC dataset.\n",
      "    ligands: The list of prepared and state enumerated ligands to use in the FEC calculation.\n",
      "    central_ligand: An optional ligand which should be considered as the center only needed for radial networks.\n",
      "        Note this ligand will be deduplicated from the list if it appears in both.\n",
      "    experimental_protocol: The name of the experimental protocol in the CDD vault that should be\n",
      "        associated with this Alchemy network.\n",
      "    target: The name of the biological target associated with this Alchemy network.\n",
      "\n",
      " Returns:\n",
      "     The planned FEC network which can be executed locally or submitted to alchemiscale.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/Documents/Software/asapdiscovery/asapdiscovery-alchemy/asapdiscovery/alchemy/schema/fec.py\n",
      "\u001b[0;31mType:\u001b[0m      method"
     ]
    }
   ],
   "source": [
    "alchemy_factory.create_fec_dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e87bf81",
   "metadata": {},
   "source": [
    "The function requires us to create a `gufe` `ProteinComponent` for the receptor and also has some optional fields which have not been covered yet. \n",
    "\n",
    "#### Optional Fields\n",
    "\n",
    "- `central_ligand`: Only needed if we are using a `RadialPlanner` as our network planner method in which case you should provide the ligand and ensure its not passed in the ligands list as well. \n",
    "\n",
    "- `experimental_protocol`: Used to associate a CDD vault experimental protocol with the dataset which allows automated retrieval of experimental data (potencies) but in general is a useful source of provenance to know what experimental data the network should be compared against.\n",
    "\n",
    "- `target`: The biological target for which the alchemical free energy network is being run for. Again, a useful source of provenance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6e502932",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joshua/mambaforge/envs/asapdiscovery/lib/python3.11/site-packages/gufe/components/explicitmoleculecomponent.py:79: UserWarning: Partial charges have been provided, these will preferentially be used instead of generating new partial charges\n",
      "  warnings.warn(wmsg)\n"
     ]
    }
   ],
   "source": [
    "from gufe.components.proteincomponent import ProteinComponent\n",
    "\n",
    "gufe_receptor = ProteinComponent.from_pdb_file('mac1-receptor.pdb')\n",
    "fec_network = alchemy_factory.create_fec_dataset(\n",
    "    dataset_name = alchemy_dataset.dataset_name,\n",
    "    receptor = gufe_receptor,\n",
    "    ligands = alchemy_dataset.posed_ligands,\n",
    "    experimental_protocol = 'MAC1-protocol',\n",
    "    target = \"SARS-CoV-2-MAC1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4215c16",
   "metadata": {},
   "source": [
    "We now have a `FreeEnergyCalculationNetwork` which contains our planned network and all of the runtime settings needed to compute the edges using OpenFE. First lets inspect the network we just generated, you can see that during the `prep` workflow the settings and software versions used are saved to ensure the workflow is reproducible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1d27b544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'atom_mapping_engine'\u001b[0m: \u001b[1m{\u001b[0m\n",
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       "        \u001b[32m'threed'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "        \u001b[32m'max3d'\u001b[0m: \u001b[1;36m1000.0\u001b[0m,\n",
       "        \u001b[32m'element_change'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "        \u001b[32m'seed'\u001b[0m: \u001b[32m''\u001b[0m,\n",
       "        \u001b[32m'shift'\u001b[0m: \u001b[3;92mTrue\u001b[0m\n",
       "    \u001b[1m}\u001b[0m,\n",
       "    \u001b[32m'scorer'\u001b[0m: \u001b[32m'default_lomap'\u001b[0m,\n",
       "    \u001b[32m'network_planning_method'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'type'\u001b[0m: \u001b[32m'MinimalRedundantPlanner'\u001b[0m, \u001b[32m'redundancy'\u001b[0m: \u001b[1;36m2\u001b[0m\u001b[1m}\u001b[0m,\n",
       "    \u001b[32m'provenance'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'openfe'\u001b[0m: \u001b[32m'0.14.0'\u001b[0m, \u001b[32m'lomap'\u001b[0m: \u001b[32m'2.0.0-rc+156.g4b1f092'\u001b[0m, \u001b[32m'rdkit'\u001b[0m: \u001b[32m'2023.09.4'\u001b[0m\u001b[1m}\u001b[0m\n",
       "\u001b[1m}\u001b[0m"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec_network.network.dict(include={\"atom_mapping_engine\", \"scorer\", \"network_planning_method\", \"provenance\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3a2fab",
   "metadata": {},
   "source": [
    "We can also view the planned network using some OpenFE tools, as we can see each of the ligands has at least two connections in the graph corresponding to the level of redundancy we requested in the network planning method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f7ae189e",
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 80\u001b[0m\u001b[1;36m0x800\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
    "%matplotlib inline\n",
    "plot_atommapping_network(fec_network.network.to_ligand_network())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20dbc025-c6e4-49a5-a38b-419b1b2b30ed",
   "metadata": {},
   "source": [
    "With the `OpenFE` CLI you can also view the `graphml` file for this network by running `openfe view-ligand-network ` which should pop up an interactive GUI view of the network for you to inspect the molecule structures and even the atom mapping per edge."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4a8fd0",
   "metadata": {},
   "source": [
    "We can also inspect the individual edges of the network and the atom mappings generated which describe how the atoms will be transformed during a simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5a654f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.Image\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapping = next(iter(fec_network.network.to_ligand_network().edges))\n",
    "mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9810e15",
   "metadata": {},
   "source": [
    "We can now save this network to file to execute later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7d81de71",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec_network.to_file('fec-network.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3238e70",
   "metadata": {},
   "source": [
    "## ASAP-Alchemy Submit\n",
    "\n",
    "We are now ready to execute our alchemical network and estimate the relative binding affinity of the ligands. Currently there are two ways to do this locally with OpenFE or on distributed compute via Alchemiscale. At ASAP we use Alchemiscale exclusively allowing us to manage thousands of simultaneous calculations across many networks across many supercomputers, however it is very easy to also execute the calculations locally. We have an interface to simply convert the network to the OpenFE `AlchemicalNetwork` object and use either the CLI or API provided by OpenFE to execute the tasks see the [tutorials](https://docs.openfree.energy/en/stable/tutorials/index.html) for more information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dba127aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mAlchemicalNetwork-1d20fd40930c029edc296479c0a6f0e2\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openfe_network = fec_network.to_alchemical_network()\n",
    "openfe_network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc98e21d",
   "metadata": {},
   "source": [
    "We provide an Alchemiscale helper within `ASAP-Alchemy` to make it easier when working with our `FreeEnergyCalculationNetworks`s, this can be used to submit, execute, restart and gather results from an alchemiscale instance and is just a wrapper around the alchemiscale client. The class does require that your login `identifier` and `key` are set as the environment variables `ALCHEMISCALE_ID` and `ALCHEMISCALE_KEY` respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fec5d7c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from drugforge.alchemy.utils import AlchemiscaleHelper\n",
    "from alchemiscale import Scope\n",
    "\n",
    "os.environ[\"ALCHEMISCALE_ID\"] = 'my-id'\n",
    "os.environ[\"ALCHEMISCALE_KEY\"] = 'my-key'\n",
    "helper = AlchemiscaleHelper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2102f3",
   "metadata": {},
   "source": [
    "We can now create the network on the Alchemiscale instance using the helper which once complete will return a new copy of our `FreeEnergyCalculationNetwork` object with a results field setup and a network key which can be used to identify the network on Alchemiscale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84f76370",
   "metadata": {},
   "outputs": [],
   "source": [
    "submitted_network = helper.create_network(planned_network=fec_network, scope=Scope(org=\"MYORG\", campaign=\"SARS_CoV_2_MAC1\", project=\"mac1_testing\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae2dc61c",
   "metadata": {},
   "source": [
    "Now we need to create tasks for each of the transformations defined in our network and action them which queues them for execution. These two stages are handled by one convince method on the helper called `action_network`. Note that we now used the `submitted_network` version of our network as it contains the `network_key` used to find the network on Alchemiscale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac36fb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "task_keys = helper.action_network(planned_network=submitted_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07895cad",
   "metadata": {},
   "source": [
    "Once all of the calculations have finished or once the results are needed the results can be gathered from alchemiscale using the `collect_results` helper function which returns a new copy of the `FreeEnergyCalculationNetwork` with results for the completed edges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc5ce1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_with_results = helper.collect_results(planned_network=submited_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4016cc",
   "metadata": {},
   "source": [
    "## ASAP-Alchemy Predict\n",
    "\n",
    "Once we have a successful set of transformations we can estimate the absolute binding affinity of our ligands by combining the relative measures via the maximum likelihood estimator method implemented in cinnabar, a best practices method for reporting the results of free energy calculations. ASAP-Alchemy has an interface to Cinnabar to make it easy to turn the simulation results into useful information for the med chem team. For this example we will use the TYK2 network which has been curated as part of the [protein-ligand benchmark](https://github.com/OpenFreeEnergy/protein-ligand-benchmark) dataset, we have the results of a subsection of this network in our testing suite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9ccad711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "\n",
       "\u001b[1;35mAlchemiscaleResults\u001b[0m\u001b[1m(\u001b[0m\n",
       "    \u001b[33mtype\u001b[0m=\u001b[32m'AlchemiscaleResults'\u001b[0m,\n",
       "    \u001b[33mresults\u001b[0m=\u001b[1m[\u001b[0m\n",
       "        \u001b[1;35mTransformationResult\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mtype\u001b[0m=\u001b[32m'TransformationResult'\u001b[0m,\n",
       "            \u001b[33mligand_a\u001b[0m=\u001b[32m'lig_ejm_31'\u001b[0m,\n",
       "            \u001b[33mligand_b\u001b[0m=\u001b[32m'lig_ejm_47'\u001b[0m,\n",
       "            \u001b[33mphase\u001b[0m=\u001b[32m'solvent'\u001b[0m,\n",
       "            \u001b[33mestimate\u001b[0m=\u001b[1m<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-27.8338026\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0371470415\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_50'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m7.03463605\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.164472206\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_43'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-20.251948\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0450103031\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_46'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m17.3107438\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0794125032\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-14.9018509\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0709130889\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_43'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-19.0483257\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0995483935\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_28'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m18.004845\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.10120943\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_46'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-40.6932954\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0946114604\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_27'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m7.34399439\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0654328618\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_28'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m17.652864\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0881101767\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_46'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m17.38679\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0587437623\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_48'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-15.867133\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.336624474\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_50'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m6.86208142\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0261312694\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_47'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-27.7222517\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.145075019\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_48'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-16.7720235\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0552824613\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_27'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'complex'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m6.9859083\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0875415962\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-15.7131154\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0488326429\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;35mTransformationResult\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'TransformationResult'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_a\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mligand_b\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_46'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mphase\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'solvent'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33mestimate\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-39.9402329\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m            \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0648095297\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie / mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m]\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[33mnetwork_key\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mScopedKey\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mgufe_key\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mGufeKey\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'AlchemicalNetwork-4617c8d8d6599124af3b4561b8d910a0'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[1m>\u001b[0m,\n",
       "        \u001b[33morg\u001b[0m=\u001b[32m'asap'\u001b[0m,\n",
       "        \u001b[33mcampaign\u001b[0m=\u001b[32m'jhorton'\u001b[0m,\n",
       "        \u001b[33mproject\u001b[0m=\u001b[32m'tyk2_asap_test'\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from drugforge.alchemy.schema.fec import FreeEnergyCalculationNetwork\n",
    "tyk2_network = FreeEnergyCalculationNetwork.from_file(fetch_test_file('tyk2_result_network.json'))\n",
    "tyk2_network.results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b594da6",
   "metadata": {},
   "source": [
    "We can see that the `tyk2_network` now has a results field which has references to the network on the alchemiscale instance and a set of `Transformationresults` which contain the results of each simulated edge of the network. As the OpenFE `RelativeHybridTopologyProtocol` simulates the `complex` and `solvent` phases separately we need to combine these to estimate the relative free energy between the ligands in the transformation. We can do this by using the interface with `cinnabar`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a4029bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[1;35mMeasurement\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mlabelA\u001b[0m=\u001b[32m'lig_ejm_31'\u001b[0m,\n",
       "        \u001b[33mlabelB\u001b[0m=\u001b[32m'lig_ejm_47'\u001b[0m,\n",
       "        \u001b[33mDG\u001b[0m=\u001b[1m<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.111550848\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.149755347\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_50'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.172554629\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.166535131\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_43'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m1.20362226\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.109251133\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_46'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0760461558\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0987784151\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_42'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.8112645\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.0861004831\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_28'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-0.351980997\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.13418924\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_46'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-0.753062541\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.114680441\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_23'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_jmc_27'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m-0.358086092\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.10929314\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mcomputational\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33msource\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'calculated'\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m    \u001b[0m\u001b[1;35mMeasurement\u001b[0m\u001b[1;39m(\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelA\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_31'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mlabelB\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'lig_ejm_48'\u001b[0m\u001b[39m,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mDG\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.904890457\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33muncertainty\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m0.341133679\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kilocalorie_per_mole'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m>,\u001b[0m\n",
       "\u001b[39m        \u001b[0m\u001b[33mtemperature\u001b[0m\u001b[39m=<\u001b[0m\u001b[1;35mQuantity\u001b[0m\u001b[1;39m(\u001b[0m\u001b[1;36m298.15\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'kelvin'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[1m>\u001b[0m,\n",
       "        \u001b[33mcomputational\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
       "        \u001b[33msource\u001b[0m=\u001b[32m'calculated'\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measures = tyk2_network.results.to_cinnabar_measurements()\n",
    "measures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e16dc36",
   "metadata": {},
   "source": [
    "We also convert directly to a `FEMap` object and use it to calculate the absolute binding affinity estimates, first let's draw a graph of the converted network to check we get the expected `9 edges` and `10 ligands`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7f82958c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 100\u001b[0m\u001b[1;36m0x1000\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fe_map = tyk2_network.results.to_fe_map()\n",
    "%matplotlib inline\n",
    "fe_map.draw_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04812d6a",
   "metadata": {},
   "source": [
    "Now we can check that the calculated relative affinities have been correctly converted and we can view them as a table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "73eacbcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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labelAlabelBDDG (kcal/mol)uncertainty (kcal/mol)sourcecomputational
0lig_ejm_31lig_ejm_470.1115510.149755calculatedTrue
1lig_ejm_31lig_ejm_420.8112650.086100calculatedTrue
2lig_ejm_31lig_ejm_46-0.7530630.114680calculatedTrue
3lig_ejm_31lig_ejm_480.9048900.341134calculatedTrue
4lig_ejm_42lig_ejm_500.1725550.166535calculatedTrue
5lig_ejm_42lig_ejm_431.2036220.109251calculatedTrue
6lig_ejm_46lig_jmc_230.0760460.098778calculatedTrue
7lig_jmc_23lig_jmc_28-0.3519810.134189calculatedTrue
8lig_jmc_23lig_jmc_27-0.3580860.109293calculatedTrue
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labelDG (kcal/mol)uncertainty (kcal/mol)sourcecomputational
0lig_ejm_31-0.1332230.075722MLETrue
1lig_ejm_47-0.0216720.153867MLETrue
2lig_ejm_420.6780410.093269MLETrue
3lig_ejm_46-0.8862860.091456MLETrue
4lig_ejm_480.7716670.314375MLETrue
5lig_ejm_500.8505960.175745MLETrue
6lig_ejm_431.8816630.135084MLETrue
7lig_jmc_23-0.8102400.110756MLETrue
8lig_jmc_28-1.1622210.163317MLETrue
9lig_jmc_27-1.1683260.147726MLETrue
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 325x325 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cinnabar.plotting import plot_DDGs, plot_DGs\n",
    "plot_DDGs(fe_map.to_legacy_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44913e0f",
   "metadata": {},
   "source": [
    "Finally we can plot the estimated absolute binding afinities vs experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d1e385c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 325x325 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_DGs(fe_map.to_legacy_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72d07da",
   "metadata": {},
   "source": [
    "Before using the predictions in production it is common to benchmark the system under study to asses its suitability for free energy calculations, this normally involves curating a set of similar ligands which have reliable experimental measures of affinity and estimating their affinity using the production protocol. To help with debugging these benchmarks we have created some tools to produce interactive graphs to make it easier to identify outliers. Here we will generate interactive equivalents of the cinnabar graphs using `ASAP-Alchemy`. First, we use a utility function which extracts the absolute and relative predictions from the cinnabar `FEMap` and inserts experimental data extracted from a formatted csv file. Note that the absolute predictions are also automatically shifted to match the experimental range of binding affinity values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3e96b602",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
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       ""
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       "
\n", "
\n", "
\n", "" ] }, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "6f53cd3a-8da9-4a99-9af8-84543f97e312" } }, "output_type": "display_data" } ], "source": [ "from drugforge.alchemy.predict import get_data_from_femap, create_absolute_report, create_relative_report\n", "fe_map = tyk2_network.results.to_fe_map()\n", "fe_map.generate_absolute_values()\n", "absolute_df, relative_df = get_data_from_femap(\n", " fe_map=fe_map,\n", " ligands=tyk2_network.network.ligands,\n", " assay_units='IC50',\n", " reference_dataset=fetch_test_file('tyk2_reference_data.csv')\n", ")" ] }, { "cell_type": "markdown", "id": "e3752bf8", "metadata": {}, "source": [ "Now we can produce an interactive relative report to inspect each transformation and identify outliers and save this to an interactive HTML file:" ] }, { "cell_type": "code", "execution_count": 60, "id": "ee3d6070", "metadata": {}, "outputs": [], "source": [ "relative_layout = create_relative_report(dataframe=relative_df)\n", "relative_layout.save(\n", " filename='tyk2-relative.html',\n", " title='tyk2-benchmark',\n", " embed=True\n", ")" ] }, { "cell_type": "markdown", "id": "5d5a40b0", "metadata": {}, "source": [ "The same can also be done for the absolute report:" ] }, { "cell_type": "code", "execution_count": 61, "id": "2b498330", "metadata": {}, "outputs": [], "source": [ "absolute_layout = create_absolute_report(dataframe=absolute_df)\n", "absolute_layout.save(\n", " filename='tyk2-absolute.html',\n", " title='tyk2-benchmark',\n", " embed=True\n", ")" ] }, { "cell_type": "markdown", "id": "727b8620", "metadata": {}, "source": [ "An interactive preview of these plots is shown below, try hovering over the points on the plot to identify the ligands involved in the transformation:" ] }, { "cell_type": "code", "execution_count": 62, "id": "ddf1808b-3087-4900-aa61-d226a6ed1e49", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", "
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const render_items = [{\"docid\":\"0e7ef165-0b67-42fe-b9c9-e1a2b424d71d\",\"roots\":{\"0c3562dd-1b0a-4851-8e9d-702e9fc0d001\":\"cc40575e-35bb-441f-b436-32b3d442c6eb\"},\"root_ids\":[\"0c3562dd-1b0a-4851-8e9d-702e9fc0d001\"]}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "0c3562dd-1b0a-4851-8e9d-702e9fc0d001" } }, "output_type": "display_data" } ], "source": [ "from bokeh.io import output_notebook, show\n", "output_notebook()\n", "from drugforge.alchemy.predict import plotmol_relative, draw_mol\n", "\n", "# manually create the plots which are generated as part of the report\n", "mols, combined_smiles, titles = [], [], []\n", "for _, data in relative_df.iterrows():\n", " smiles = \".\".join([data[\"SMILES_A\"], data[\"SMILES_B\"]])\n", " combined_smiles.append(smiles)\n", " mols.append(draw_mol(smiles=smiles))\n", " titles.append((data[\"labelA\"], data[\"labelB\"]))\n", "relative_df[\"Molecules\"] = mols\n", "relative_df[\"labels\"] = titles\n", "relative_df[\"smiles\"] = combined_smiles\n", "# create a plotting dataframe which drops rows with nans\n", "plotting_df = relative_df.dropna(axis=0, inplace=False)\n", "plotting_df.reset_index(inplace=True)\n", "fig = plotmol_relative(\n", " calculated=plotting_df[\"DDG (kcal/mol) (FECS)\"],\n", " experimental=plotting_df[\"DDG (kcal/mol) (EXPT)\"],\n", " smiles=plotting_df[\"smiles\"],\n", " titles=plotting_df[\"labels\"],\n", " calculated_uncertainty=plotting_df[\"uncertainty (kcal/mol) (FECS)\"],\n", " experimental_uncertainty=plotting_df[\"uncertainty (kcal/mol) (EXPT)\"],\n", " )\n", "show(fig)" ] }, { "cell_type": "code", "execution_count": null, "id": "19e2d76e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }