用于合成约束多目标先导化合物优化的模块化智能体框架
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
AI总结：
本研究提出开源模块化智能体框架SABLE，结合LLM与专用工具实现合成约束下的多目标先导化合物优化，可高效富集符合计算目标的候选集，为早期药物发现提供决策支持。
AI中文摘要：
先导化合物优化需要针对效力、选择性、物理化学性质、药代动力学、安全性及合成约束等相互竞争的指标，迭代设计命中化合物类似物。我们提出SABLE（Synthetically-accessible Agentic Bayesian Ligand Exploration，可合成智能体贝叶斯配体探索），这是一个开源框架，采用自然语言编排指导化学结构优化。SABLE利用大语言模型（LLM）解析用户定义的目标并分配任务，同时由专用工具执行反应模板化类似物枚举、物理化学及ADMET性质预测、基于结构的亲和力打分和贝叶斯优化。该工作流是设计-合成-测试-分析循环中分析和优先级排序阶段的计算孪生，为每个数值输出提供溯源。在单目标和多目标优化研究中，SABLE在仅评估枚举搜索空间子集的情况下，丰富了符合用户定义计算目标的候选集。其模块化架构允许通过编辑简单配置文件替换工具和表征后端，无需修改操作逻辑。SABLE为早期药物发现中优先考虑受合成约束的类似物提供了可扩展的决策支持框架。
英文摘要：
Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints. We present SABLE (Synthetically-accessible Agentic Bayesian Ligand Exploration), an open-source framework that employs natural-language orchestration to guide chemical structure optimization. SABLE uses an LLM to interpret user-defined goals and route tasks, while specialized tools perform reaction-templated analog enumeration, physicochemical and ADMET property prediction, structure-based affinity scoring, and Bayesian optimization. The resulting workflow is a computational twin of the analytical and prioritization stages of the design-make-test-analyze cycle, providing provenance of each numerical output. Across single, and multi-objective optimization studies, SABLE enriches candidate sets for user-defined computational objectives while evaluating only a subset of the enumerated search space. Its modular architecture allows tools and characterization backends to be replaced by editing a simple config file, without modifying operational logic. SABLE provides an extensible decision-support framework for prioritizing synthetically constrained analogs in early-stage drug discovery.
