Predicting compound activity from phenotypic profiles and chemical structures

Nikita Moshkov; Tim Becker; Kevin Yang; Peter Horvath; Vlado Dancik; Bridget K. Wagner; Paul A. Clemons; Shantanu Singh; Anne E. Carpenter; Juan C. Caicedo

doi:10.1038/s41467-023-37570-1

Nature Communications (Apr 2023)

Predicting compound activity from phenotypic profiles and chemical structures

Nikita Moshkov,
Tim Becker,
Kevin Yang,
Peter Horvath,
Vlado Dancik,
Bridget K. Wagner,
Paul A. Clemons,
Shantanu Singh,
Anne E. Carpenter,
Juan C. Caicedo

Affiliations

Nikita Moshkov: Broad Institute of MIT and Harvard
Tim Becker: Broad Institute of MIT and Harvard
Kevin Yang: University of California
Peter Horvath: Biological Research Centre
Vlado Dancik: Broad Institute of MIT and Harvard
Bridget K. Wagner: Broad Institute of MIT and Harvard
Paul A. Clemons: Broad Institute of MIT and Harvard
Shantanu Singh: Broad Institute of MIT and Harvard
Anne E. Carpenter: Broad Institute of MIT and Harvard
Juan C. Caicedo: Broad Institute of MIT and Harvard

DOI: https://doi.org/10.1038/s41467-023-37570-1
Journal volume & issue: Vol. 14, no. 1
pp. 1 – 11

Abstract

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Experimental assays are used to determine if compounds cause a desired activity in cells. Here the authors demonstrate that computational methods can predict compound bioactivity given their chemical structure, imaging and gene expression data from historic screening libraries.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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