Cell morphological representations of genes enhance prediction of drug targets

Identifying a chemical’s mechanism of action on biological systems is critical for drug development. A compound’s mechanism can sometimes be identified by matching its image-based morphological profile to a well-annotated library. We enhance this approach by incorporating gene representations, demonstrating improved classification performance compared to direct matching methods. These representations are generated using morphological profiles of cells with artificially altered gene expression. A transformer model is trained to classify gene-compound pairs as true or false interactions, using both gene and compound profiles. The model then ranks likely target genes for unseen compounds. The strategy performs well for compounds targeting known genes but has limited effectiveness for novel targets, likely due to the current dataset size. However, the performance increase over existing profile-matching methods is notable. Future work with expanded datasets may enhance predictive capabilities, potentially accelerating early-stage drug discovery by improving target identification for novel compounds.