标题： 一种整合大型语言模型和化学片段空间的协作框架：对先导化合物设计的相互启发 显示英文标题

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

摘要： 组合优化算法在计算机辅助药物设计中是必不可少的，通过逐步探索化学空间来设计与靶蛋白具有高亲和力的先导化合物。 然而，当前方法在整合领域知识方面面临固有的挑战，限制了它们在识别具有新颖且有效结合模式的先导化合物方面的性能。 在此，我们提出 AutoLeadDesign，这是一种利用大型语言模型中编码的化学片段来广泛整合领域知识的先导化合物设计框架，以逐步实现对广阔化学空间的有效探索。 全面的实验表明，AutoLeadDesign优于基线方法。 显著的是，针对两个临床上相关的靶点（PRMT5 和 SARS-CoV-2 PLpro）的实证先导设计活动展示了 AutoLeadDesign 在从头生成先导化合物方面的能力，达到了专家水平的设计效果。 结构分析进一步证实了它们机制验证的抑制模式。 通过追踪设计过程，我们发现 AutoLeadDesign 与基于片段的药物设计具有类似机制，而传统上后者依赖于专家决策，这进一步揭示了其有效的原因。 总体而言，AutoLeadDesign 提供了一种高效的先导化合物设计方法，表明其在药物设计中的潜在应用价值。 显示英文摘要

摘要： Combinatorial optimization algorithm is essential in computer-aided drug design by progressively exploring chemical space to design lead compounds with high affinity to target protein. However current methods face inherent challenges in integrating domain knowledge, limiting their performance in identifying lead compounds with novel and valid binding mode. 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 the expert decision-making, further revealing why it works. Overall, AutoLeadDesign offers an efficient approach for lead compounds design, suggesting its potential utility in drug design.