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, literature-grounded question answering via retrieval-augmented generation, molecular generation, multi-property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation. The agent autonomously retrieved relevant biomolecular information, including FASTA sequences, SMILES representations, and literature, and answered mechanistic questions with improved contextual accuracy compared to standard LLMs. It then generated chemically diverse seed molecules and predicted 75 properties, including ADMET-related and general physicochemical descriptors, which guided iterative molecular refinement. Across two refinement rounds, the number of molecules with QED > 0.6 increased from 34 to 55. The number of molecules satisfying empirical drug-likeness filters also rose; for example, compliance with the Ghose filter increased from 32 to 55 within a pool of 100 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.

翻译：我们提出了一种由大语言模型（LLMs）驱动的模块化框架，该框架能够自动化并优化早期计算药物发现流程中的关键任务。通过将LLM推理与领域专用工具相结合，该框架实现了生物医学数据检索、基于文献的检索增强生成问答、分子生成、多属性预测、属性感知的分子优化以及3D蛋白质-配体结构生成。该代理能够自主检索相关生物分子信息，包括FASTA序列、SMILES表示和文献资料，并以比标准LLM更高的上下文准确性回答机理问题。随后，它生成了化学多样性种子分子，并预测了75种属性，包括ADMET相关及一般物理化学描述符，这些属性指导了迭代分子优化。经过两轮优化，QED > 0.6的分子数量从34个增加到55个。满足经验性药物相似性过滤器的分子数量也有所上升；例如，在100个分子池中，符合Ghose过滤器的分子从32个增至55个。该框架还利用Boltz-2生成了3D蛋白质-配体复合物，并为候选化合物提供了快速的结合亲和力估计。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。其模块化设计能够灵活集成不断发展的工具和模型，为人工智能辅助的治疗发现提供了可扩展的基础。