To reduce runtime, this “Quick start” guide uses a fast semiempirical model, “GFN2-xTB”, to generate training data, rather than the “default” method used to train mainline OpenFF force fields.
BespokeFit aims to provide an automated pipeline to augment a molecular mechanics force field with highly specific force field parameters trained to accurately capture the important features and phenomenology of an input set of molecules. It produces bespoke torsion parameters that have been trained to capture as closely as possible the torsion profiles of the rotatable bonds in the target molecule, which collectively have a large impact on conformational preferences.
The recommended way to install
openff-bespokefit is via the
mamba package manager. There are several optional
dependencies, and a good starting environment is:
mamba create -n bespokefit -y -c conda-forge mamba python=3.9 mamba activate bespokefit mamba install -y -c conda-forge openff-bespokefit xtb-python ambertools
although several other methods are available.
The fastest way to start producing a bespoke force field for your molecule of interest is through the command-line interface. A full list of the available commands, as well as help about each, can be viewed by running:
openff-bespoke executor --help
run command is most useful if you are wanting to perform a quick one-off bespoke fit for a single molecule using
a temporary bespoke executor.
You should only have one
run command running at once. If you want to compute bespoke parameters for multiple
molecules at once see the section on production fits.
It will accept either a SMILES pattern:
openff-bespoke executor run --smiles "CC(=O)NC1=CC=C(C=C1)O" \ --workflow "default" \ --output "acetaminophen.json" \ --output-force-field "acetaminophen.offxml" \ --n-qc-compute-workers 2 \ --qc-compute-n-cores 1 \ --default-qc-spec xtb gfn2xtb none
Or the path to an SDF (or similar) file:
openff-bespoke executor run --file "acetaminophen.sdf" \ --workflow "default" \ --output "acetaminophen.json" \ --output-force-field "acetaminophen.offxml" \ --n-qc-compute-workers 2 \ --qc-compute-n-cores 1 \ --default-qc-spec xtb gfn2xtb none
run command also takes arguments defining how the bespoke fit should be performed and parallelized.
Sometimes bespoke commands will raise
RuntimeError: The gateway could not be reached. This can usually be resolved
by rerunning the command a few times.
Here we have specified that we wish to start the fit from the general OpenFF 2.0.0 (Sage) force field, augmenting it with bespoke parameters generated according to the default built-in workflow using GFN2-xTB reference data.
Other available workflows can be viewed by running
openff-bespoke executor run --help, and the path to a
saved workflow factory can also be provided using the
Alternatively, certain options defined by the workflow can be overridden from the CLI. For example, the default
specification to use for any new QC calculations can be specified using the
--default-qc-spec flag, e.g.
--default-qc-spec xtb gfn2xtb none. See the
--help for other available overrides.
By default, BespokeFit will use create a single worker for each step in the fitting workflow (i.e. one for fragmenting larger molecules, one for generating any needed reference QC data, and one for doing the final bespoke fit), however extra workers can easily be requested to speed things up:
openff-bespoke executor run --file "acetaminophen.sdf" \ --workflow "default" \ --n-fragmenter-workers 2 \ --n-optimizer-workers 2 \ --n-qc-compute-workers 2 \ --qc-compute-n-cores 1 \ --default-qc-spec xtb gfn2xtb none
See the chapter on the bespoke executor for more information about parallelizing fits.
To create bespoke parameters for multiple molecules, such as a particular lead series, it is recommended to instead launch a dedicated bespoke executor. This allows BespokeFit to re-use data from previous fits, such as common QC calculations, and easily retrieve previous bespoke fits.
The first step is to launch a bespoke executor. The executor is the workhorse of BespokeFit; it coordinates every step of the fitting workflow from molecule fragmentation to QC data generation:
openff-bespoke executor launch --n-fragmenter-workers 1 \ --n-optimizer-workers 2 \ --n-qc-compute-workers 4 \ --qc-compute-n-cores 1
The number of workers dedicated to each bespoke fitting stage can be configured here. In general, we recommend devoting most of your compute power to the QC compute stage, as this stage is both the most expensive and the most parallelizable. See the chapter on the bespoke executor for more information about parallelizing fits.
Once the executor has been launched, we can submit molecules to the executor for optimization with the
command. Molecules can be specified either in the form of a SMILES pattern:
openff-bespoke executor submit --smiles "CC(=O)NC1=CC=C(C=C1)O" \ --workflow "default" \ --default-qc-spec xtb gfn2xtb none
Or by loading the molecule from an SDF (or similar) file:
openff-bespoke executor submit --file "acetaminophen.sdf" \ --workflow "default" \ --default-qc-spec xtb gfn2xtb none
submit command also accepts a combination of the two input forms, as well as multiple occurrences of either. After
the molecules are successfully submitted, the executor will print a table which maps a unique molecule ID to each
molecule SMILES or input file. These IDs can later be used to query the executor about the molecule. This table can be
saved to a .CSV file by adding the
--save-submission-info flag to the command.
A particular fitting procedure can be monitored with the
openff-bespoke executor watch --id "1"
A full list of submissions currently being processed can be printed with the
openff-bespoke executor list
list can be filtered by status; for example, if you would only like to inspect those that have failed:
openff-bespoke executor list --status errored
Once finished, the final force field can be retrieved using the
openff-bespoke executor retrieve --id "1" \ --output "acetaminophen.json" \ --force-field "acetaminophen.offxml"
See the results chapter for more details on retrieving the results of a bespoke fit.
For users who prefer Python or who are looking for more control over how the fit will be performed, BespokeFit exposes a full Python API.
At the heart of the fitting pipeline is the
BespokeWorkflowFactory encodes all of
the settings that will feed into and control the bespoke fitting pipeline for any input molecule. The workflow
factory transforms a particular molecule into a workflow, which fully describes how bespoke
parameters will be generated for that specific molecule:
from openff.bespokefit.workflows import BespokeWorkflowFactory from openff.qcsubmit.common_structures import QCSpec factory = BespokeWorkflowFactory( # Define the starting force field that will be augmented with bespoke # parameters. initial_force_field="openff-2.0.0.offxml", # Change the level of theory that the reference QC data is generated at default_qc_specs=[ QCSpec( method="gfn2xtb", basis=None, program="xtb", spec_name="xtb", spec_description="gfn2xtb", ) ] )
Similar to the previous steps, here we override the default
“default” QC specification to use GFN2-xTB. If we had Psi4
installed, we could remove the
default_qc_specs argument and the factory would instead use our mainline
fitting QC method.
The default factory will produce workflows that augment the OpenFF 2.0.0 force field
with bespoke torsion parameters for all non-terminal rotatable bonds in the molecule that have been trained
to quantum chemical torsion scan data generated for said molecule.
See the configuration section for more info on customizing the workflow factory.
from openff.toolkit.topology import Molecule input_molecule = Molecule.from_smiles("C(C(=O)O)N") # Glycine workflow_schema = factory.optimization_schema_from_molecule( molecule=input_molecule )
This schema encodes the full workflow that will produce the bespoke parameters for this specific molecule, including how any reference QC data should be generated and at what level of theory, the types of bespoke parameters to generate, hyperparameters defining how they should be trained, and the sequence of fitting steps that should be performed (e.g. fit a charge model, then re-fit the torsion and valence parameters using the new charge model).
Such a schema is fed into a
BespokeExecutor that will run the full workflow:
from openff.bespokefit.executor import BespokeExecutor, BespokeWorkerConfig, wait_until_complete with BespokeExecutor( n_fragmenter_workers = 1, n_optimizer_workers = 1, n_qc_compute_workers = 2, qc_compute_worker_config=BespokeWorkerConfig(n_cores=1) ) as executor: # Submit our workflow to the executor task_id = executor.submit(input_schema=workflow_schema) # Wait until the executor is done output = wait_until_complete(task_id) if output.status == "success": # Save the resulting force field to an OFFXML file output.bespoke_force_field.to_file("output-ff.offxml") elif output.status == "errored": # OR the print the error message if unsuccessful print(output.error)
BespokeExecutor not only takes care of calling out to any external programs in your workflow such as when
generating reference QC data, it also manages spreading a queue of tasks over a pool of worker threads so that fitting
can be executed efficiently in parallel. The
BespokeExecutor is described in more detail in
its own chapter.
There workflow factory is largely customizable in order to accommodate different fitting experiments or protocols that you may wish to use:
from openff.qcsubmit.common_structures import QCSpec from openff.bespokefit.schema.optimizers import ForceBalanceSchema from openff.bespokefit.schema.smirnoff import ProperTorsionHyperparameters from openff.bespokefit.schema.targets import TorsionProfileTargetSchema factory = BespokeWorkflowFactory( # Define the starting force field that will be augmented with bespoke # parameters. initial_force_field="openff-2.0.0.offxml", # Select the underlying optimization engine. optimizer=ForceBalanceSchema( max_iterations=50, penalty_type="L1" ), # Define the types of bespoke parameter to generate and hyper-parameters # that control how they will be fit, as well as the target reference data # that should be used in the fit. parameter_hyperparameters=[ProperTorsionHyperparameters()], target_templates=[TorsionProfileTargetSchema()], # Change the level of theory that the reference QC data is generated at default_qc_specs=[ QCSpec( method="gfn2xtb", basis=None, program="xtb", spec_name="xtb", spec_description="gfn2xtb", ) ] )
Once the factory is configured, it can be saved
factory.to_file("workflow-factory.yaml") # or .json
and loaded from disk easily
factory = BespokeWorkflowFactory.from_file("workflow-factory.yaml")
This makes it simple to record and share complex configurations. OpenFF recommends making this file available when publishing data generated using the outputs of BespokeFit for reproducibility. Factories that have been saved to disk can also be used via BespokeFit’s command-line interface.
Check the API docs for full descriptions of the factory’s configurable options.