A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design

Drug design, particularly in the discovery of lead compounds, is of core strategic importance to combating disease and enhancing human well-being. Prevailing computational methods, however, struggle to effectively integrate domain-specific knowledge, severely limiting their capacity to identify novel lead compounds with validated binding modes and new scaffolds. Here, we propose AutoLeadDesign, a lead compounds design framework that inspires extensive domain knowledge encoded in large language models with chemical fragments to progressively implement efficient exploration of vast chemical space. The comprehensive experiments indicate that AutoLeadDesign outperforms baseline methods. Significantly, empirical lead design campaigns targeting two clinically relevant targets (PRMT5 and SARS-CoV-2 PLpro) demonstrate AutoLeadDesign's competence in de novo generation of lead compounds, achieving expert-competitive design efficacy. Structural analysis further confirms their mechanism-validated inhibitory patterns. By tracing the process of design, we find that AutoLeadDesign shares analogous mechanisms with fragment-based drug design, which traditionally rely on expert decision-making, further revealing why it works. Overall, AutoLeadDesign offers an efficient approach for lead compound design, suggesting its potential utility in drug design.