| Literature DB >> 29503208 |
Jaak Simm1, Günter Klambauer2, Adam Arany1, Marvin Steijaert3, Jörg Kurt Wegner4, Emmanuel Gustin4, Vladimir Chupakhin4, Yolanda T Chong4, Jorge Vialard4, Peter Buijnsters4, Ingrid Velter4, Alexander Vapirev5, Shantanu Singh6, Anne E Carpenter6, Roel Wuyts7, Sepp Hochreiter2, Yves Moreau1, Hugo Ceulemans8.
Abstract
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.Entities:
Keywords: Bayesian matrix factorization; computational chemistry; deep learning; drug discovery; high-content imaging; high-throughput screening; machine learning; matrix factorization
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Year: 2018 PMID: 29503208 PMCID: PMC6031326 DOI: 10.1016/j.chembiol.2018.01.015
Source DB: PubMed Journal: Cell Chem Biol ISSN: 2451-9448 Impact factor: 8.116