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.