Sanford Burnham Prebys and collaborators unveiled DeepTarget, an AI‑driven tool that predicts anti‑cancer mechanisms of small molecules and proposes candidate targets beyond the one‑drug/one‑target paradigm. Published in npj Precision Oncology, DeepTarget applies integrative computational models to prioritize likely mechanisms and guide experimental validation. The platform combines multi‑omic data, chemical fingerprinting, and network biology to infer drug–target interactions, aiming to accelerate target identification and repurposing. The authors position DeepTarget as a resource to reduce R&D uncertainty and speed preclinical decision‑making. Adoption will hinge on prospective validation and integration with lab workflows; if robust, DeepTarget could compress early discovery timelines and reshape how biotechs prioritize lead optimization.