DeepTarget predicts anti-cancer mechanisms of action of small molecules by integrating drug and genetic screens.

Identifying the mechanisms of action (MOA) driving a drug's anti-cancer efficacy is critical for its clinical success, guiding the search for its best biomarkers, indications and combinations. Yet, systematically identifying MOAs remains challenging due to drugs often engaging multiple targets with varying affinities across different cellular contexts. Addressing this challenge, we present DeepTarget, a computational tool that integrates large-scale drug and genetic knockdown viability screens with omics data to predict a drug's MOAs driving its cancer cell killing. To test its performance, we curated eight datasets of high-confidence drug-target pairs focused on cancer drugs and benchmarked DeepTarget. We show that DeepTarget outperforms recent tools in predicting drug targets and their mutation-specificity, achieving strong predictive performance across diverse validation datasets. We experimentally validate DeepTarget's predictions in two case studies: (a) Demonstrating that pyrimethamine, an anti-parasitic drug, affects cellular viability through modulation of mitochondrial function, specifically the oxidative phosphorylation pathway, and (b) Confirming that T790-mutated EGFR mediates ibrutinib response in BTK-negative solid tumors. Additionally, we demonstrate that kinase inhibitors predicted by DeepTarget to have higher target specificity show increased progression in clinical trials. We provide DeepTarget as an open-source tool ( https://github.com/CBIIT-CGBB/DeepTarget ) along with predicted target profiles for 1,500 cancer-related drugs and 33,000 unpublished natural product extracts. DeepTarget represents a significant computational advancement among target discovery methods that complements the leading structure-based methods by considering cellular context and can potentially accelerate drug development and repurposing efforts in oncology.