diff --git a/rbfe_tutorial/ligand_network.graphml b/rbfe_tutorial/ligand_network.graphml
new file mode 100644
index 0000000..89411eb
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+++ b/rbfe_tutorial/ligand_network.graphml
@@ -0,0 +1,73 @@
+
+
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+
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+ {"score": 0.36787944117144233}
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+ {"score": 0.33287108369807955}
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+ {"score": 0.9048374180359595}
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+ {"score": 0.9048374180359595}
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+ [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], [10, 10], [11, 11], [12, 12], [13, 13], [14, 14], [15, 15], [16, 16], [17, 17], [18, 18], [19, 19], [20, 20], [21, 21], [22, 22], [23, 23], [24, 24], [25, 26], [26, 25], [27, 29], [29, 27], [30, 28], [31, 31], [32, 32], [33, 33], [34, 34], [35, 35]]
+ {"score": 0.9048374180359595}
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+ [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], [10, 10], [11, 11], [12, 12], [13, 13], [14, 14], [15, 15], [16, 16], [17, 17], [18, 18], [19, 19], [20, 20], [21, 21], [22, 22], [23, 23], [24, 24], [25, 26], [26, 25], [27, 29], [29, 27], [30, 28], [31, 31], [32, 32], [33, 33], [34, 34], [35, 35]]
+ {"score": 0.9048374180359595}
+
+
+
\ No newline at end of file
diff --git a/rbfe_tutorial/python_tutorial.ipynb b/rbfe_tutorial/python_tutorial.ipynb
deleted file mode 100644
index d0abb6a..0000000
--- a/rbfe_tutorial/python_tutorial.ipynb
+++ /dev/null
@@ -1,581 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "35354229",
- "metadata": {},
- "source": [
- "# Setting up a relative binding free energy network\n",
- "\n",
- "This tutorial gives a step-by-step process to set up a relative binding free energy (RBFE) simulation campaign using OpenFE. This tutorial is designed as an accompaniment to the CLI tutorial found in the same directory as this notebook.\n",
- "\n",
- "With the CLI, all the steps here were performed by the `openfe plan-rbfe-network` command. However, that command offers little room for customization. Using the Python interface gives us the ability to customize all aspects of how our simulation runs. This tutorial provides a step-by-step Python guide to reproducing the setup done in the CLI tutorial, highlighting areas where the Python interface enables customization."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "fc97de03",
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "import openfe"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2fea29c3",
- "metadata": {},
- "source": [
- "## Loading the ligands\n",
- "\n",
- "First we must load the chemical models between which we wish to calculate free energies.\n",
- "In this example these are initially stored in a molfile (`.sdf`) containing multiple molecules.\n",
- "This can be loaded using the `SDMolSupplier` class from rdkit and passed to openfe."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "41cf8be7",
- "metadata": {},
- "outputs": [],
- "source": [
- "from rdkit import Chem\n",
- "supp = Chem.SDMolSupplier(\"tyk2_ligands.sdf\", removeHs=False)\n",
- "ligands = [openfe.SmallMoleculeComponent.from_rdkit(mol) for mol in supp]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8e5de19a",
- "metadata": {},
- "source": [
- "## Charging the ligands\n",
- "\n",
- "It is recommended to use a single set of charges for each ligand to ensure reproducibility between repeats or consistent charges between different legs of a calculation involving the same ligand, like a relative binding affinity calculation for example. \n",
- "\n",
- "Here we will use some utility functions from OpenFE which can assign partial charges to a series of molecules with a variety of methods which can be configured via the `OpenFFPartialChargeSettings` class. In this example \n",
- "we will charge the ligands using the `am1bcc` method from `ambertools` which is the default charge scheme used by OpenFE."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5219106c",
- "metadata": {},
- "outputs": [],
- "source": [
- "from openfe.protocols.openmm_utils.omm_settings import OpenFFPartialChargeSettings\n",
- "from openfe.protocols.openmm_utils.charge_generation import bulk_assign_partial_charges\n",
- "\n",
- "charge_settings = OpenFFPartialChargeSettings(partial_charge_method=\"am1bcc\", off_toolkit_backend=\"ambertools\")\n",
- "\n",
- "charged_ligands = bulk_assign_partial_charges(\n",
- " molecules=ligands,\n",
- " overwrite=False, \n",
- " method=charge_settings.partial_charge_method,\n",
- " toolkit_backend=charge_settings.off_toolkit_backend,\n",
- " generate_n_conformers=charge_settings.number_of_conformers,\n",
- " nagl_model=charge_settings.nagl_model,\n",
- " processors=1\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6963be83",
- "metadata": {},
- "source": [
- "## Creating the `LigandNetwork`\n",
- "\n",
- "The first step is to create a `LigandNetwork`, which is a network with small molecules as nodes, and atom mappings, the description of how to alchemically mutate between the molecules, as its edges.\n",
- "\n",
- "The pipeline for creating a `LigandNetwork` can involve three components:\n",
- "\n",
- "* **Atom Mapper**: Proposes potential atom mappings (descriptions of the alchemical change) for pairs of ligands. We will use the `LomapAtomMapper`.\n",
- "* **Scorer**: Given an atom mapping, provides an estimate of the quality of that mapping (higher scores are better). We will use `default_lomap_scorer`.\n",
- "* **Network Planner**: Creates the actual `LigandNetwork`; different network planners provide different strategies. We will create a minimal spanning network with the `generate_minimal_spanning_network` method.\n",
- "\n",
- "Each of these components could be replaced by other options."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "5a3cf244",
- "metadata": {},
- "outputs": [],
- "source": [
- "mapper = openfe.LomapAtomMapper(max3d=1.0, element_change=False)\n",
- "scorer = openfe.lomap_scorers.default_lomap_score\n",
- "network_planner = openfe.ligand_network_planning.generate_minimal_spanning_network"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "acc13581",
- "metadata": {},
- "source": [
- "The exact call signature depends on the network planner: a minimal spanning network requires a score, whereas that is optional for a radial network (but a radial network needs the central ligand to be provided)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "f6e7bce5",
- "metadata": {},
- "outputs": [],
- "source": [
- "ligand_network = network_planner(\n",
- " ligands=charged_ligands,\n",
- " mappers=[mapper],\n",
- " scorer=scorer\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b7492637",
- "metadata": {},
- "source": [
- "Now we can look at the overall structure of the `LigandNetwork`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "e6ca6131",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
- "plot_atommapping_network(ligand_network)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5f99678c",
- "metadata": {},
- "source": [
- "We can also inspect the individual atom mappings:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "c55cbcac",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# get the first edge; it automatically displays in a Jupyter notebook\n",
- "mapping = next(iter(ligand_network.edges))\n",
- "mapping"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd0c96d7",
- "metadata": {},
- "source": [
- "To get the score for this mapping, we inspect its `annotations` attribute. Arbitrary annotations can be added when a mapping is created, although our network generator only includes the score."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "6b7492d7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'score': 0.33287108369807955}"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# higher score is better\n",
- "mapping.annotations"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30b276b6",
- "metadata": {},
- "source": [
- "You can output the ligand network to the same `graphml` format as we saw in the CLI tutorial with the following:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "2263838f",
- "metadata": {},
- "outputs": [],
- "source": [
- "with open(\"ligand_network.graphml\", mode='w') as f:\n",
- " f.write(ligand_network.to_graphml())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "056924a3",
- "metadata": {},
- "source": [
- "## Creating a single `Transformation`\n",
- "\n",
- "The `LigandNetwork` only knows about the small molecules and the alchemical connections between them. It doesn't know anything about environment (e.g., solvent) or about the `Protocol` that will be used during the simulation.\n",
- "\n",
- "That information in included in a `Transformation`. Each of these transformations corresponds to a single leg of the simulation campaign, so for each edge in the `LigandNetwork`, we will create two `Transformation`s: one for the complex and one for solvent.\n",
- "\n",
- "In practice, this will be done for each edge of the `LigandNetwork` in a loop, but for illustrative purposes we'll dive into the details of creating a single transformation. In particular, we'll create the solvent leg for the pair of molecules we selecting for the mapping above."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d0cb1329",
- "metadata": {},
- "source": [
- "### Creating `ChemicalSystem`s\n",
- "\n",
- "OpenFE describes complex molecular systems as being composed of `Component`s. For example, we have `SmallMoleculeComponent` for each small molecule in the `LigandNetwork`. We'll create a `SolventComponent` to describe the solvent, and binding free energy calculations involve a `ProteinComponent`.\n",
- "\n",
- "The `Component`s are joined in a `ChemicalSystem`, which describes all the particles in the simulation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "9d2fbc22",
- "metadata": {},
- "outputs": [],
- "source": [
- "# defaults are water with NaCl at 0.15 M\n",
- "solvent = openfe.SolventComponent()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "3f1706ee",
- "metadata": {},
- "outputs": [],
- "source": [
- "protein = openfe.ProteinComponent.from_pdb_file(\"./tyk2_protein.pdb\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "710285ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "systemA = openfe.ChemicalSystem({\n",
- " 'ligand': mapping.componentA,\n",
- " 'solvent': solvent,\n",
- " 'protein': protein\n",
- "})\n",
- "systemB = openfe.ChemicalSystem({\n",
- " 'ligand': mapping.componentB,\n",
- " 'solvent': solvent,\n",
- " 'protein': protein \n",
- "})"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "340d1a6e",
- "metadata": {},
- "source": [
- "### Creating a `Protocol`\n",
- "\n",
- "The actual simulation is performed by a `Protocol`. We'll use an OpenMM-based hybrid topology relative free energy `Protocol`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "3f394a0d",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3bddfa3c",
- "metadata": {},
- "source": [
- "The easiest way to customize protocol settings is to start with the default settings, and modify them. Many settings carry units with them."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "fb839094",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "298.15 kelvin"
- ],
- "text/latex": [
- "$298.15\\ \\mathrm{kelvin}$"
- ],
- "text/plain": [
- "298.15 "
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "settings = RelativeHybridTopologyProtocol.default_settings()\n",
- "settings.thermo_settings.temperature # display default value"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "e83630f0",
- "metadata": {},
- "outputs": [],
- "source": [
- "from openff.units import unit\n",
- "\n",
- "# change the value\n",
- "settings.thermo_settings.temperature = 310.0 * unit.kelvin"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "56658a3a",
- "metadata": {},
- "source": [
- "We'll use the default settings for the protocol we'll use later, to match the behavior of the CLI."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "7adf42d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "default_settings = RelativeHybridTopologyProtocol.default_settings()\n",
- "protocol = RelativeHybridTopologyProtocol(default_settings)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "318ff872",
- "metadata": {},
- "source": [
- "### Creating the `Transformation`\n",
- "\n",
- "Once we have the mapping, the two `ChemicalSystem`s, and the `Protocol`, creating the `Transformation` is easy:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "44ba94ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "transformation = openfe.Transformation(\n",
- " systemA,\n",
- " systemB,\n",
- " protocol,\n",
- " mapping={'ligand': mapping},\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4283dfe4",
- "metadata": {},
- "source": [
- "To summarize, this `Transformation` contains:\n",
- "- chemical models of both sides of the alchemical transformation in `systemA` and `systemB`\n",
- "- the correspondence of items in these two sides in `mapping` \n",
- "- a description of the exact computational algorithm to use to perform the estimate in `protocol`"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1e29d1c8",
- "metadata": {},
- "source": [
- "## Creating the `AlchemicalNetwork`\n",
- "\n",
- "The `AlchemicalNetwork` contains all the information needed to run the entire campaign. It consists of a `Transformation` for each leg of the campaign. We'll loop over all the mappings, and then loop over the legs. In that inner loop, we'll make each transformation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "66666a80",
- "metadata": {},
- "outputs": [],
- "source": [
- "transformations = []\n",
- "for mapping in ligand_network.edges:\n",
- " for leg in ['solvent', 'complex']:\n",
- " # use the solvent and protein created above\n",
- " sysA_dict = {'ligand': mapping.componentA,\n",
- " 'solvent': solvent}\n",
- " sysB_dict = {'ligand': mapping.componentB,\n",
- " 'solvent': solvent}\n",
- " \n",
- " if leg == 'complex':\n",
- " sysA_dict['protein'] = protein\n",
- " sysB_dict['protein'] = protein\n",
- " \n",
- " # we don't have to name objects, but it can make things (like filenames) more convenient\n",
- " sysA = openfe.ChemicalSystem(sysA_dict, name=f\"{mapping.componentA.name}_{leg}\")\n",
- " sysB = openfe.ChemicalSystem(sysB_dict, name=f\"{mapping.componentB.name}_{leg}\")\n",
- " \n",
- " prefix = \"rbfe_\" # prefix is only to exactly reproduce CLI\n",
- " \n",
- " transformation = openfe.Transformation(\n",
- " stateA=sysA,\n",
- " stateB=sysB,\n",
- " mapping={'ligand': mapping},\n",
- " protocol=protocol, # use protocol created above\n",
- " name=f\"{prefix}{sysA.name}_{sysB.name}\"\n",
- " )\n",
- " transformations.append(transformation)\n",
- "\n",
- "network = openfe.AlchemicalNetwork(transformations)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6c61fe36",
- "metadata": {},
- "source": [
- "## Writing the `AlchemicalNetwork` to disk\n",
- "\n",
- "We'll write out each transformation to disk, so that they can be run independently using the `openfe quickrun` command:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "d6cebd9a",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pathlib\n",
- "# first we create the directory\n",
- "transformation_dir = pathlib.Path(\"transformations\")\n",
- "transformation_dir.mkdir(exist_ok=True)\n",
- "\n",
- "# then we write out each transformation\n",
- "for transformation in network.edges:\n",
- " transformation.dump(transformation_dir / f\"{transformation.name}.json\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "b96b57a9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "rbfe_lig_ejm_31_complex_lig_ejm_42_complex.json\n",
- "rbfe_lig_ejm_31_complex_lig_ejm_46_complex.json\n",
- "rbfe_lig_ejm_31_complex_lig_ejm_47_complex.json\n",
- "rbfe_lig_ejm_31_complex_lig_ejm_48_complex.json\n",
- "rbfe_lig_ejm_31_complex_lig_ejm_50_complex.json\n",
- "rbfe_lig_ejm_31_solvent_lig_ejm_42_solvent.json\n",
- "rbfe_lig_ejm_31_solvent_lig_ejm_46_solvent.json\n",
- "rbfe_lig_ejm_31_solvent_lig_ejm_47_solvent.json\n",
- "rbfe_lig_ejm_31_solvent_lig_ejm_48_solvent.json\n",
- "rbfe_lig_ejm_31_solvent_lig_ejm_50_solvent.json\n",
- "rbfe_lig_ejm_42_complex_lig_ejm_43_complex.json\n",
- "rbfe_lig_ejm_42_solvent_lig_ejm_43_solvent.json\n",
- "rbfe_lig_ejm_46_complex_lig_jmc_23_complex.json\n",
- "rbfe_lig_ejm_46_complex_lig_jmc_27_complex.json\n",
- "rbfe_lig_ejm_46_complex_lig_jmc_28_complex.json\n",
- "rbfe_lig_ejm_46_solvent_lig_jmc_23_solvent.json\n",
- "rbfe_lig_ejm_46_solvent_lig_jmc_27_solvent.json\n",
- "rbfe_lig_ejm_46_solvent_lig_jmc_28_solvent.json\n"
- ]
- }
- ],
- "source": [
- "!ls transformations/"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c30e8ae2",
- "metadata": {},
- "source": [
- "Each of these individual `.json` files contains a `Transformation`, which contains all the information to run the calculation. These could be farmed out as individual jobs on a HPC cluster. These files are identical to what were created in setup stage of the CLI tutorial; for details on running them, follow from the section on running simulations in the CLI tutorial"
- ]
- }
- ],
- "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": "3.11.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/rbfe_tutorial/python_tutorial.ipynb b/rbfe_tutorial/python_tutorial.ipynb
new file mode 120000
index 0000000..4902ef9
--- /dev/null
+++ b/rbfe_tutorial/python_tutorial.ipynb
@@ -0,0 +1 @@
+rbfe_python_tutorial.ipynb
\ No newline at end of file
diff --git a/rbfe_tutorial/rbfe_python_tutorial.ipynb b/rbfe_tutorial/rbfe_python_tutorial.ipynb
index d0abb6a..5bbd5d3 100644
--- a/rbfe_tutorial/rbfe_python_tutorial.ipynb
+++ b/rbfe_tutorial/rbfe_python_tutorial.ipynb
@@ -62,10 +62,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "5219106c",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Generating charges: 100%|███████████████████████| 10/10 [02:59<00:00, 17.95s/it]\n"
+ ]
+ }
+ ],
"source": [
"from openfe.protocols.openmm_utils.omm_settings import OpenFFPartialChargeSettings\n",
"from openfe.protocols.openmm_utils.charge_generation import bulk_assign_partial_charges\n",
@@ -103,7 +111,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"id": "5a3cf244",
"metadata": {},
"outputs": [],
@@ -123,7 +131,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "f6e7bce5",
"metadata": {},
"outputs": [],
@@ -145,18 +153,18 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"id": "e6ca6131",
"metadata": {},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
""
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -176,13 +184,13 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "c55cbcac",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -207,17 +215,17 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "6b7492d7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'score': 0.33287108369807955}"
+ "{'score': 0.9048374180359595}"
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -237,7 +245,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "2263838f",
"metadata": {},
"outputs": [],
@@ -274,7 +282,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"id": "9d2fbc22",
"metadata": {},
"outputs": [],
@@ -285,7 +293,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"id": "3f1706ee",
"metadata": {},
"outputs": [],
@@ -295,7 +303,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"id": "710285ca",
"metadata": {},
"outputs": [],
@@ -324,7 +332,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"id": "3f394a0d",
"metadata": {
"scrolled": true
@@ -344,7 +352,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"id": "fb839094",
"metadata": {},
"outputs": [
@@ -357,10 +365,10 @@
"$298.15\\ \\mathrm{kelvin}$"
],
"text/plain": [
- "298.15 "
+ ""
]
},
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -372,7 +380,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"id": "e83630f0",
"metadata": {},
"outputs": [],
@@ -388,18 +396,23 @@
"id": "56658a3a",
"metadata": {},
"source": [
- "We'll use the default settings for the protocol we'll use later, to match the behavior of the CLI."
+ "We use the default settings with an adapted solvent padding for the complex phase to avoid adding too many waters."
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"id": "7adf42d6",
"metadata": {},
"outputs": [],
"source": [
- "default_settings = RelativeHybridTopologyProtocol.default_settings()\n",
- "protocol = RelativeHybridTopologyProtocol(default_settings)"
+ "# Create a protocol for the solvent legs using default settings\n",
+ "solvent_protocol = RelativeHybridTopologyProtocol(RelativeHybridTopologyProtocol.default_settings())\n",
+ "\n",
+ "# Create a prrotocol for the complex legs with a reduced solvent padding\n",
+ "complex_settings = RelativeHybridTopologyProtocol.default_settings()\n",
+ "complex_settings.solvation_settings.solvent_padding = 1 * unit.nanometer\n",
+ "complex_protocol = RelativeHybridTopologyProtocol(complex_settings)"
]
},
{
@@ -414,16 +427,17 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 17,
"id": "44ba94ca",
"metadata": {},
"outputs": [],
"source": [
+ "# Here we assume we are creating a transformation for a complex leg\n",
"transformation = openfe.Transformation(\n",
" systemA,\n",
" systemB,\n",
- " protocol,\n",
- " mapping={'ligand': mapping},\n",
+ " complex_protocol,\n",
+ " mapping=mapping,\n",
")"
]
},
@@ -435,7 +449,7 @@
"To summarize, this `Transformation` contains:\n",
"- chemical models of both sides of the alchemical transformation in `systemA` and `systemB`\n",
"- the correspondence of items in these two sides in `mapping` \n",
- "- a description of the exact computational algorithm to use to perform the estimate in `protocol`"
+ "- a description of the exact computational algorithm to use to perform the estimate in `complex_protocol`"
]
},
{
@@ -450,7 +464,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 18,
"id": "66666a80",
"metadata": {},
"outputs": [],
@@ -465,8 +479,14 @@
" 'solvent': solvent}\n",
" \n",
" if leg == 'complex':\n",
+ " # If this is a complex transformation we use the complex protocol\n",
+ " # and add in the protein to the chemical states\n",
+ " protocol = complex_protocol\n",
" sysA_dict['protein'] = protein\n",
" sysB_dict['protein'] = protein\n",
+ " else:\n",
+ " # If this is a solvent transformation we just use the solvent protocol\n",
+ " protocol = solvent_protocol\n",
" \n",
" # we don't have to name objects, but it can make things (like filenames) more convenient\n",
" sysA = openfe.ChemicalSystem(sysA_dict, name=f\"{mapping.componentA.name}_{leg}\")\n",
@@ -477,7 +497,7 @@
" transformation = openfe.Transformation(\n",
" stateA=sysA,\n",
" stateB=sysB,\n",
- " mapping={'ligand': mapping},\n",
+ " mapping=mapping,\n",
" protocol=protocol, # use protocol created above\n",
" name=f\"{prefix}{sysA.name}_{sysB.name}\"\n",
" )\n",
@@ -498,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 19,
"id": "d6cebd9a",
"metadata": {},
"outputs": [],
@@ -510,12 +530,12 @@
"\n",
"# then we write out each transformation\n",
"for transformation in network.edges:\n",
- " transformation.dump(transformation_dir / f\"{transformation.name}.json\")"
+ " transformation.to_json(transformation_dir / f\"{transformation.name}.json\")"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 20,
"id": "b96b57a9",
"metadata": {},
"outputs": [
@@ -573,7 +593,112 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.11.14"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {
+ "030a9a234df04a2d8585393a38bc75ad": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "bar_style": "success",
+ "layout": "IPY_MODEL_e85c46b9e6cd4b4aa6721d42d95f43ae",
+ "max": 45,
+ "style": "IPY_MODEL_f88d8e81d56d42a597caa294df13b463",
+ "value": 45
+ }
+ },
+ "10e8431bce284e53adcc12f212cc9d70": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLStyleModel",
+ "state": {
+ "description_width": "",
+ "font_size": null,
+ "text_color": null
+ }
+ },
+ "498d5b0fc56749e59ef58c37d6d532bc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "layout": "IPY_MODEL_ee4133a6dace4dde806e0d7abb02685e",
+ "style": "IPY_MODEL_10e8431bce284e53adcc12f212cc9d70",
+ "value": "Mapping: 100%"
+ }
+ },
+ "5df4176b643243e4b0b09a61b29ac397": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "children": [
+ "IPY_MODEL_498d5b0fc56749e59ef58c37d6d532bc",
+ "IPY_MODEL_030a9a234df04a2d8585393a38bc75ad",
+ "IPY_MODEL_7c62d22f8c7542b9b2263b34cf14ead9"
+ ],
+ "layout": "IPY_MODEL_657b6894bf6f433e878640fd8b9d8c7f"
+ }
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+ "657b6894bf6f433e878640fd8b9d8c7f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "2.0.0",
+ "model_name": "LayoutModel",
+ "state": {}
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+ "7c62d22f8c7542b9b2263b34cf14ead9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "2.0.0",
+ "model_name": "HTMLModel",
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+ "style": "IPY_MODEL_96dc11073263403985645abb3375a8da",
+ "value": " 45/45 [00:00<00:00, 151.07it/s]"
+ }
+ },
+ "96dc11073263403985645abb3375a8da": {
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