English(EN) A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization

新框架利用LLM进行AI驱动的药物发现优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-13 04:00

研究人员开发了SABLE（Synthetically-accessible Agentic Bayesian Ligand Exploration），一个开源框架，旨在简化药物发现中的苗头化合物优化。SABLE利用自然语言指令指导化学结构设计，将LLM与用于类似物枚举、属性预测和贝叶斯优化的专用工具集成。该模块化系统通过为分析和优先级排序阶段提供计算孪生体，旨在加速设计-制造-测试-分析（design-make-test-analyze）循环，最终丰富目标计算对象的候选集。 AI

影响该框架可以通过提高计算设计和合成类似物优先级排序的效率，来加速药物发现的早期阶段。

排序理由该集群包含一篇详细介绍特定科学应用新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland, Matthew Hart, Steini Davidsson, Angelos Angelopoulos, Ron Alterovitz, Marcello DeLuca, Alexander Tropsha · 2026-08-13 04:00

用于合成约束多目标苗头化合物优化的模块化代理框架

arXiv:2608.11483v1 Announce Type: new Abstract: 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 Bayesia…

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用于合成约束多目标苗头化合物优化的模块化代理框架

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