Rapid traversal of vast chemical space using machine learning-guided docking screens

The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.

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Publication Details

Journal
Nature Computational Science
Published
2025-03-13
DOI
https://doi.org/10.1038/s43588-025-00777-x
Citations
42
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
61.68

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article

Rapid traversal of vast chemical space using machine learning-guided docking screens

Yurii S. Moroz, Nour Aldin Kahlous, Israel Cabeza de Vaca, Andreas Luttens et al.
42 citations
Nature Computational Science
Computational Drug Discovery Methods
61.68
article

Rapid traversal of vast chemical space using machine learning-guided docking screens

Yurii S. Moroz, Nour Aldin Kahlous, Israel Cabeza de Vaca, Andreas Luttens, Dmytro S. Radchenko, Jose Manuel Brea, Marı́a Isabel Loza, Antón L. Martínez, Ulf Norinder, Jens Carlsson, Leonard Sparring
article en
42 citations

Abstract

The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.

Nature Computational ScienceVol. 5(4)
Broad Institute (US), Uppsala University (SE), Taras Shevchenko National University of Kyiv (UA), Universidade de Santiago de Compostela (ES), Science for Life Laboratory (SE), Center for Research in Molecular Medicine and Chronic Diseases (ES), Instituto de Investigación Sanitaria de Santiago (ES), Enamine (Ukraine) (UA), Massachusetts Institute of Technology (US)
Eli Lilly and Company, European Commission, Cancerfonden, Linköpings Universitet, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Uppsala Universitet, Xunta de Galicia, European Regional Development Fund, Agencia Estatal de Investigación, National Supercomputer Centre, Linköpings Universitet
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Openalex Percentile: Top 0%
Computational Drug Discovery Methods
61.68
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