ADAPT

ADAPT (Advanced Drug Discovery Automation Platform) is a modular, AI-native system that unifies semantic literature mining, 3D structural modeling, predictive chemistry, and automated workflows to compress time-to-discovery while lowering cost and experimental attrition. Compared with traditional, fragmented tools and manual curation, ADAPT addresses key bottlenecks end-to-end: AI knowledge-graph mining (DeepSage, EuNet, Vizit) accelerates insight extraction; geometry/topology-aware target selection (Matisse) improves hit relevance; similarity-driven validation (Similis, Battleship) reduces false positives; multi-objective optimization (Flocking, Modica, Pareto) balances potency, ADMET, and synthesizability; and reactivity-aware retrosynthesis (Retro, Fukui, Deco) streamlines route design. The platform natively integrates laboratory robotics and domain databases for closed-loop experimentation, and is fully extensible to external engines and proprietary models, enabling rapid customization across pharma, nutraceuticals, cosmetics, and agrochemicals. In practice, this holistic, automation-ready architecture delivers faster, more precise, and traceable progression from literature to validated preclinical candidates than conventional point solutions.