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

Godinez Navarro, William, Trifonov, Vladimir, Fang, Bin, Kuzu, Guray, Pei, Amy, Guiguemde, Armand, Martin, Eric, King, Frederick, Jenkins, Jeremy and Skewes-Cox, Peter (2024) Compound activity prediction with dose-dependent transcriptomic profiles and deep learning. Journal of Chemical Information and Modeling. ISSN 1549-95961549-960X


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.

Item Type: Article
Date Deposited: 19 Mar 2024 00:46
Last Modified: 19 Mar 2024 00:46