Paper | Tutorials | Examples | Demo

MolScore contains code to score de novo compounds in the context of generative de novo design by generative models via the subpackage molscore, as well as, facilitate downstream evaluation via the subpackage moleval. An objective is defined via a JSON file which can be shared to propose new multi-parameter objectives for drug design. MolScore can be used in several ways:

1. To implement a multi-parameter objective to for prospective drug design.
2. To benchmark objectives/generative models/optimization using benchmark mode (MolScoreBenchmark).
3. To implement a sequence of objectives using curriculum mode (MolScoreCurriculum).

Contributions and/or ideas for added functionality are welcomed!

Install MolScore with PyPI (recommended):

pip install molscore --upgrade

or directly from GitHub:

git clone https://github.com/MorganCThomas/MolScore.git
cd MolScore ; pip install -e .

Note: I recommend mamba for environment handling

Simplest integration of MolScore requires a config file, for example:

from molscore import MolScore
ms = MolScore(
    model_name='test',
    task_config='molscore/configs/QED.json',
    budget=10000
)
while not ms.finished:
    scores = ms.score(SMILES)

Note: see tutorial for more detail

A GUI exists to help write the config file, which can be run with the following command.

molscore_config

Note: see tutorial for more detail

Benchmarks are lists of objectives (configuration files) with metrics calculated upon exit. Re-implementations of existing benchmarks are available as presets.

from molscore import MolScoreBenchmark
msb = MolScoreBenchmark(
    model_name='test',
    output_dir='./',
    benchmark='GuacaMol',
    budget=10000
)
for task in msb:
    while not task.finished:
        scores = task.score(SMILES)

Current benchmarks available include: GuacaMol, GuacaMol_Scaffold, MolOpt, 5HT2A_PhysChem, 5HT2A_Selectivity, '5HT2A_Docking', LibINVENT Exp1&3, MolExp(L)

Note: inspect preset benchmarks with MolScoreBenchmark.presets.keys()

Note: see tutorial for more detail

The moleval subpackage can be used to calculate metrics for an arbitrary set of molecules.

from moleval.metrics.metrics import GetMetrics
MetricEngine = GetMetrics(
    test=TEST_SMILES, # Model training data subset
    train=TRAIN_SMILES, # Model training data
    target=TARGET_SMILES, # Exemplary target data
)
metrics = MetricEngine.calculate(
    GEN_SMILES, # Generated data
)

Note: see tutorial for more detail

• Curriculum learning (see tutorial)

• Experience replay buffers (see tutorial)

• Parallelisation (see tutorial)

• A GUI for monitoring generated molecules (see below)

  molscore_monitor

If you use this software, please cite it as below.

@article{thomas2024molscore,
title={MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design},
author={Thomas, Morgan and O’Boyle, Noel M and Bender, Andreas and De Graaf, Chris},
journal={Journal of Cheminformatics},
volume={16},
year={2024},
publisher={BMC}
}

This software was also utilised in the following publications:

1. Thomas, M., Smith, R.T., O’Boyle, N.M. et al. Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 13, 39 (2021). https://doi.org/10.1186/s13321-021-00516-0
2. Thomas M, O'Boyle NM, Bender A, de Graaf C. Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation. J Cheminform 14, 68 (2022). https://doi.org/10.1186/s13321-022-00646-z
3. Handa K, Thomas M, Kageyama M, Iijima T, Bender A. On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data. J Cheminform 15, 112 (2023). https://doi.org/10.1186/s13321-023-00781-1
4. Thomas M, Ahmad M, Tresadern G, de Fabritiis G. PromptSMILES: Prompting for scaffold decoration and fragment linking in chemical language models. J Cheminform 16, 77 (2024). https://doi.org/10.1186/s13321-024-00866-5
5. Bou A, Thomas M, Dittert S, Ramírez CN, Majewski M, Wang Y, Patel S, Tresadern G, Ahmad M, Moens V, Sherman W. ACEGEN: Reinforcement learning of generative chemical agents for drug discovery. J Chem Inf Model 64, 15 (2024). https://doi.org/10.1021/acs.jcim.4c00895
6. Thomas M, Matricon PG, Gillespie RJ, Napiórkowska M, Neale H, Mason JS, Brown J, Fieldhouse C, Swain NA, Geng T, O'Boyle NM. Identification of nanomolar adenosine A2A receptor ligands using reinforcement learning and structure-based drug design. Nat Commun 16, 5485 (2025). https://doi.org/10.1038/s41467-025-60629-0