MolScore 1.5.5

A scoring, benchmarking and evaluation framework for goal directed generative models

MolScore: A scoring, evaluation and benchmarking framework for de novo drug design

Overview

MolScore contains code to score and benchmark 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 benchmark objectives, or to conduct multi-parameter objectives for drug design. Generative models with MolScore integrated can be found here.

Contributions and/or ideas for added functionality are welcomed!

Installation

MolScore can be installed by cloning this repository and setting up an environment using your favourite environment manager (I recommend mamba).

git clone https://github.com/MorganCThomas/MolScore.git
cd MolScore
mamba env create -f environment.yml
mamba activate molscore
pip install ./

Note: You can use pip install -e ./ to install in develop mode.

Alternatively, MolScore is available via the Python Package Index.

pip install molscore --upgrade

Functionality

Scoring functionality present in molscore, some scoring functions require external softwares and necessary licenses.

Type	Method
Docking	Glide, Smina, OpenEye, GOLD, PLANTS, rDock, Vina, Gnina
Ligand preparation	RDKit->Epik, Moka->Corina, Ligprep, Gypsum-DL
3D Similarity	ROCS, Open3DAlign
2D Similarity	Fingerprint similarity (any RDKit fingerprint and similarity measure), substructure match/filter, Applicability domain
Predictive models	Scikit-learn (classification/regression), PIDGINv5a, ChemProp, ADMET-AI
Synthesizability	RAscore, AiZynthFinder, SAscore, ReactionFilters (Scaffold decoration)
Descriptors	RDKit, Maximum consecutive rotatable bonds, Penalized LogP, LinkerDescriptors (Fragment linking) etc.
Transformation methods	Linear, linear threshold, step threshold, Gaussian
Aggregation methods	Arithmetic mean, geometric mean, weighted sum, product, weighted product, auto-weighted sum/product, pareto front
Diversity filters	Unique, Occurence, memory assisted + ScaffoldSimilarityECFP

^a PIDGINv5 is a suite of pre-trained RF classifiers on ~2,300 ChEMBL31 targets

Performance metrics present in moleval, many of which are from GuacaMol or MOSES.

Type	metric
Intrinsic property	Validity, Uniqueness, Scaffold uniqueness, Internal diversity (1 & 2), Sphere exclusion diversityb, Solow Polasky diversityg, Scaffold diversity, Functional group diversityc, Ring system diversityc, Filters (MCF & PAINS), Purchasabilityd
Extrinsic propertya	Novelty, FCD, Analogue similaritye, Analogue coverageb, Functional group similarity, Ring system similarity, Single nearest neighbour similarity, Fragment similarity, Scaffold similarity, Outlier bits (Silliness)f, Wasserstein distance (LogP, SA Score, NP score, QED, Weight)

^a In reference to a specified external dataset
^b As in our previous work here
^c Adaption based on Zhang et al.
^d Using molbloom
^e Similar to Blaschke et al.
^f Based on SillyWalks by Pat Walters
^g Based on Liu et al.

Usage

For further details, we refer you to the tutorials. Here is a snapshot of using MolScore with the GUIs available.

Here is a GIF demonstrating writing a config file with the help of the GUI, running MolScore in a mock example (scoring randomly sampled SMILES), and monitoring the output with another GUI.

Once molscore has been implemented into a generative model, the objective needs to be defined! Writing a JSON file is a pain though so instead a streamlit app is provided do help. Simply call molscore_config from the command line (a simple wrapper to streamlit run molscore/gui/config.py)

Once the configuration file is saved, simply point to this file path and run de novo molecule optimization. If running with the monitor app you'll be able to investigate molecules as they're being generated. Simply call molscore_monitor from the command line (a wrapper to streamlit run molscore/gui/monitor.py).

Citation & Publications

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

@article{thomas2023molscore,
title={MolScore: A scoring, evaluation and benchmarking framework for de novo drug design},
author={Thomas, Morgan and O'Boyle, Noel M and Bender, Andreas and de Graaf, Chris},
journal={ChemRxiv},
year={2023}
}

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. ChemRxiv (2024). https://doi.org/10.26434/chemrxiv-2024-z5jnt/