Package: guacamol
GuacaMolGuacaMolis an open source Python package for benchmarking of models forde novomolecular design.For an in-depth explanation of the types of benchmarks and baseline scores,
please consult our paperBenchmarking Models for De Novo Molecular DesignInstallationThe easiest way to installguacamolis withpip:pipinstallguacamolDependencies:guacamolrequires theRDKit library(version2018.09.1.0or newer).We also depend on theFCDlibrary (version1.1) for the calculation of the Fréchet ChemNet Distance.Unit testing suiteYou can test your installation of the guacamol benchmarking library by running the unit tests from this directory:pytest.Benchmarking modelsFor the distribution-learning benchmarks, specializeDistributionMatchingGenerator(fromguacamol.distribution_matching_generator) for your model.
Instances of this class must be able to generate molecules similar to the training set.For the actual benchmarks, callassess_distribution_learning(fromguacamol.assess_distribution_learning) with an instance of your class.
You must also provide the location of the training set file (See section "Data" below).For the goal-directed benchmarks, specializeGoalDirectedGenerator(fromguacamol.goal_directed_generator) for your model.
Instances of this class must be able to generate a specified number of molecules
that achieve high scores for a given scoring function.For the actual benchmarks, callassess_goal_directed_generation(fromguacamol.assess_goal_directed_generation) with an instance of your class.Example implementations for baseline methods are available fromhttps://github.com/BenevolentAI/guacamol_baselines.Inguacamol_baselines,
we provide aDockerfilewith an example environment for developing generative models and running guacamol.DataFor fairness in the evaluation of the benchmarks and comparability of the results,
you should use a training set containing molecules from the ChEMBL dataset.
Follow the procedure described below to get standardized datasets.DownloadYou can download pre-built datasetshere:md505ad85d871958a05c02ab51a4fde8530trainingmd5e53db4bff7dc4784123ae6df72e3b1f0validationmd5677b757ccec4809febd83850b43e1616testmd57d45bc95c33c10cb96ef5e78c38ac0b6allGenerationTo generate the training data yourself, runpython -m guacamol.data.get_data -o [output_directory]which will download and process ChEMBL for you in your current folder.This script will use the molecules fromholdout_set_gcm_v1.smilesas a holdout set, and will exclude molecules very similar to these.Different versions of your Python packages may lead to differences in the generated dataset, which will cause the script to fail.
See the section below ("Docker") to reproducibly generate the standardized dataset with the hashes given above.DockerTo be sure that you have the right dependencies you can build a Docker image, run from the top-level directory:docker build -t guacamol-deps -f dockers/Dockerfile .Then you can run:docker run --rm -it  -v `pwd`:/guacamol -w /guacamol guacamol-deps python -m guacamol.data.get_data -o guacamol/dataChange log1 May 2020: update version of FCD dependency15 Oct 2020: pin dependencies since FCD does not10 Nov 2021: relax pinned versions of keras, tensorflow & h5py dependencies20 Dec 2021: expose forbidden symbols argument for custom smiles dataset filteringLeaderboardSeehttps://benevolent.ai/guacamol.