posted on 2024-01-31, 08:55authored byWilliam J. Godinez, Vladimir Trifonov, Bin Fang, Guray Kuzu, Luying Pei, W. Armand Guiguemde, Eric J. Martin, Frederick J. King, Jeremy L. Jenkins, Peter Skewes-Cox
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 toward 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 the
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 2692 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 submicromolar
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.