Two computational advances aim to narrow the gap between in‑silico predictions and actionable drug development. Sanford Burnham and NIH teams released DeepTarget, which integrates drug and genetic screens with omics to predict mechanisms of action for anti‑cancer small molecules and outperformed several existing tools in benchmark tests. DeepTarget can nominate primary and secondary targets across cell contexts. Terray Therapeutics unveiled EMMI, an AI selection model designed to rank computationally generated molecules for synthesis decisions, reducing wasted chemistry by prioritizing candidates with higher experimental success probabilities. Both approaches address the bottleneck from virtual molecule pools to physical testing. Researchers said these tools improve target deconvolution and candidate triage, supporting biomarker selection and combination strategies. Adoption will depend on transparent benchmarks, prospective validation, and integration into medicinal chemistry and CMC decision pipelines.