用AI框架自动优化药物候选分子，兼顾合成可行性与多重药理指标。
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
- 通过自然语言指令调度化学结构优化任务，结合贝叶斯方法智能搜索
- 在有限计算量下显著提升符合多目标约束的候选分子比例
- 模块化设计可灵活替换工具，适合早期药物研发团队使用
从先导化合物优化到候选药物需在活性、选择性、理化性质、药代动力学、安全性及合成可行性等多重约束下迭代设计。我们提出开源框架SABLE（合成可及性代理贝叶斯配体探索），利用大模型解析用户目标并协调任务流程，结合反应模板生成类似物、理化性质与ADMET预测、基于结构的亲和力评分及贝叶斯优化等专用工具。该工作流是设计-合成-测试-分析循环中分析与优先级判断阶段的计算镜像，可追溯每项数值输出来源。在单目标与多目标优化研究中，SABLE在仅评估部分枚举空间的情况下，显著提升了符合用户定义计算目标的候选集质量。其模块化架构支持通过修改配置文件替换工具与表征后端，无需改动核心逻辑。SABLE为早期药物发现中优先筛选合成可行类似物提供了可扩展的决策支持框架。
原文摘要 · Abstract (English)
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.
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