Ligand biological activity predicted by cleaning positive and negative chemical correlations

Proceedings of the National Academy of Sciences of the United States of America
Alpha A LeeDavid A Price

Abstract

Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chemically dissimilar to all known ligands. The hit rate of our model is significantly higher than the state of the art. Our model can be interpreted and visualized to offer chemical insights about the molecular motifs that are synergistic or antagonistic to M1 agonism, which we have prospectively experimentally verified.

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Citations

Sep 26, 2019·Angewandte Chemie·Connor W ColeyKlavs F Jensen
Feb 11, 2020·Frontiers in Chemistry·Tânia F G G Cova, Alberto A C C Pais
Jan 14, 2021·Biotechnology and Bioprocess Engineering : BBE·Hyunho KimHojung Nam
Jul 17, 2021·Journal of Chemical Information and Modeling·Giorgio Amendola, Sandro Cosconati
Aug 14, 2021·Chemical Reviews·Bing Huang, O Anatole von Lilienfeld
Jul 31, 2019·Journal of Chemical Information and Modeling·Kevin YangRegina Barzilay
Dec 29, 2020·Accounts of Chemical Research·W Patrick Walters, Regina Barzilay
Jun 22, 2019·ACS Medicinal Chemistry Letters·Qingyi YangLei Zhang
Aug 14, 2020·Journal of Chemical Information and Modeling·Todd J WillsAlpha A Lee

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