AI-MedCraft: A Strategy-Driven AI Platform for Multi-Objective Molecular Design

Small-molecule drug discovery requires balancing potency, selectivity, solubility, safety, and synthetic feasibility, among other interdependent properties. Yet most computational workflows address these properties sequentially or collapse them into fixed weighted scores. Although artificial intelligence has accelerated molecular design, many platforms struggle to handle explicit multiobjective trade-offs. Here, we present AI-MedCraft, a strategy-driven molecular design framework that applies adaptive, Pareto-guided reinforcement learning to optimize multiple objectives concurrently within a unified workflow. When structural information is available, physics-aware scoring is incorporated to support binding-competent designs. In a structure-based benchmark on the solubility rescue of the BTK inhibitor GDC-0834, AI-MedCraft achieves broader Pareto-front coverage than the scalar-reward reinforcement learning framework REINVENT 4 under matched objectives and computational cost. In a second case study, AI-MedCraft redesigns Efavirenz to retain HIV-1 reverse transcriptase engagement while reducing predicted 5-HT2A off-target binding, demonstrating its use in multiconstraint molecular optimization.