AI-Assisted Medicinal Chemistry & Active Learning — Hands-on

Build AI-assisted medicinal chemistry workflows that prioritize the right compounds faster. This hands-on module teaches active learning for design–make–test–analyze (DMTA) , uncertainty quantification, acquisition strategies for single/multi-objective optimization, and human-in-the-loop review. You will ship a closed-loop pipeline that proposes compounds under synthesis-aware constraints and learns from each experimental round.