Employing a structure-activity-driven approach, our goal is to develop novel small molecules using an unbiased drug development engine. We leverage biological activity data from known molecules to train both machine learning (ML) and artificial-intelligence (AI) models that can efficiently design novel molecules and perform in silico screenings.