标题： 用于药物发现中模块化任务执行的大语言模型代理 显示英文标题

标题： Large Language Model Agent for Modular Task Execution in Drug Discovery

摘要： 我们提出了一种由大型语言模型（LLMs）驱动的模块化框架，该框架自动化并简化了早期阶段计算药物发现流程中的关键任务。 通过将LLM推理与领域特定工具相结合，该框架执行生物医学数据检索、领域特定问题回答、分子生成、属性预测、属性感知分子优化以及3D蛋白质-配体结构生成。 在针对淋巴细胞白血病中BCL-2的案例研究中，该智能体自主检索了相关的生物分子信息，包括FASTA序列、SMILES表示和文献，并以比标准LLM更高的上下文准确性回答了机制性问题。 随后，它生成了化学多样性的起始分子，并预测了67个ADMET相关属性，从而指导了迭代分子优化。 在两轮优化中，QED > 0.6的分子数量从34增加到55，至少通过五项经验药物相似性规则中的四项的分子数量从29增加到52，在194个分子的库中。 该框架还使用Boltz-2生成3D蛋白质-配体复合物，并为候选化合物提供快速结合亲和力估计。 这些结果表明，该方法有效支持了分子筛选、优先级排序和结构评估。 其模块化设计使得可以灵活集成不断演进的工具和模型，为AI辅助治疗发现提供了可扩展的基础。 显示英文摘要

摘要： We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, domain-specific question answering, molecular generation, property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation. In a case study targeting BCL-2 in lymphocytic leukemia, the agent autonomously retrieved relevant biomolecular information-including FASTA sequences, SMILES representations, and literature-and answered mechanistic questions with improved contextual accuracy over standard LLMs. It then generated chemically diverse seed molecules and predicted 67 ADMET-related properties, which guided iterative molecular refinement. Across two refinement rounds, the number of molecules with QED > 0.6 increased from 34 to 55, and those passing at least four out of five empirical drug-likeness rules rose from 29 to 52, within a pool of 194 molecules. The framework also employed Boltz-2 to generate 3D protein-ligand complexes and provide rapid binding affinity estimates for candidate compounds. These results demonstrate that the approach effectively supports molecular screening, prioritization, and structure evaluation. Its modular design enables flexible integration of evolving tools and models, providing a scalable foundation for AI-assisted therapeutic discovery.