Explore the powerful AIDD (Artificial Intelligence Drug Design) capabilities within ADMET Predictor. This video covers the full generative chemistry workflow, including setting up seed molecules, applying chemical transformations, and enforcing scaffold queries. Learn how to use Pareto optimization to balance up to five competing objectives, such as PIC50 potency, synthetic difficulty, and oral bioavailability. The tutorial also demonstrates advanced features like integrating external scoring functions (e.g., AutoDock), setting “capping values” to maintain population diversity, and using intermediate results to track the evolutionary path of successful drug candidates.