Drug Discovery

Drug discovery aims to find and develop molecules that act on disease-relevant targets or biological processes, while balancing efficacy, safety, and the ability to make them. AI methods include graph and 3D models for predicting molecular properties, binding, and toxicity; generative models for proposing or editing molecules; and knowledge graphs and omics models for prioritizing targets and interpreting biological responses. Search and design methods increasingly combine these predictions with docking, synthesis constraints, experimental data, and tools coordinated by AI agents. A central challenge is that strong results on standard benchmarks may not transfer to unseen targets, chemistry, or assays, and computational scores still need reliable experimental validation.