ForesightMol: Chemistry-Guided Retrieval-Augmented Generation for Structure-Based Drug Design

Retrieval-augmented molecular generation uses existing molecules to guide ligand construction, but reference compatibility does not guarantee favorable downstream properties or chemically feasible growth. Ranking the resulting candidates solely by docking score may further limit the diversity of chemical alternatives retained under fixed experimental budgets. We propose ForesightMol, a chemistry-guided framework that integrates outcome-aware reference assessment, feasible molecular growth, and complementary candidate selection. A scientific agent leverages pretrained scientific knowledge to interpret retrieved evidence, prioritize references, and guide molecular growth, while paired short-horizon rollouts assess their downstream effects. Evidence from accepted references guides local growth decisions, while shared valence and connectivity constraints exclude infeasible extensions during both reference assessment and generation. A multi-objective set selection procedure then balances molecular quality, predicted binding compatibility, and structural diversity within a fixed delivery budget. Experiments on 100 CrossDocked2020 pockets show improvements of 29.4% in mean QED and 14.8% in scaffold diversity over the best baseline. Controlled analyses identify contributions from both generation and selection, supporting the framework's effectiveness in delivering diverse, high-quality molecular candidates.