Compound activity prediction with dose-dependent transcriptomic profiles and deep learning

Godinez, W. J.; Trifonov, V.; Fang, B.; Kuzu, G.; Pei, L.; Guiguemde, W. A.; Martin, E. J.; King, F. J.; Jenkins, J. L.; Skewes-Cox, P.

doi:10.1101/2023.08.03.551883

bioRxiv · 10.1101/2023.08.03.551883

Compound activity prediction with dose-dependent transcriptomic profiles and deep learning

Godinez, W. J.·Trifonov, V.·Fang, B.·Kuzu, G.·Pei, L.·Guiguemde, W. A.·Martin, E. J.·King, F. J.·Jenkins, J. L.·Skewes-Cox, P.

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Abstract

Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene-expression signatures from a single profiling assay have shown promise towards predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce Transcriptomics-to-Activity Transformer (TAT) models that leverage gene-expression profiles observed over compound treatment at multiple concentrations to predict compound activity in other biochemical or cellular assays. We built TAT models based on gene-expression data from a RASL-Seq assay to predict the activity of 2,692 compounds in 262 dose response assays. We obtained useful models for 51% of the assays as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several sub-micromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.

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Godinez, W. J., Trifonov, V., Fang, B., Kuzu, G., Pei, L., Guiguemde, W. A., Martin, E. J., King, F. J., Jenkins, J. L., Skewes-Cox, P.. 2023-08-06. Compound activity prediction with dose-dependent transcriptomic profiles and deep learning. https://doi.org/10.1101/2023.08.03.551883

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