用AI智能代理自动发现药物，70%目标能生成达标分子
LIDDIA: Language-based Intelligent Drug Discovery Agent
- 基于大模型推理，自主规划药物发现流程
- 在30个靶点中70%以上生成符合药学标准的分子
- 可发现前列腺/乳腺癌新候选药，适合医药研发者使用
药物发现耗时长、成本高，依赖经验丰富的化学家在庞大的潜在疗法空间中探索。尽管人工智能在化学领域取得进展，但缺乏能全流程自主导航的智能代理。为此，我们提出LIDDIA，一个可在计算机上自主运行的智能药物发现代理。通过利用大语言模型的推理能力，LIDDIA成为低成本且高度可适配的自动化药物发现工具。我们全面评估显示：（1）在30个临床相关靶点中，超过70%的尝试生成了符合关键药学标准的分子；（2）能智能平衡化学空间中的探索与利用；（3）成功识别出针对AR/NR3C4这一重要靶点的新型候选分子，该靶点与前列腺癌和乳腺癌密切相关。代码与数据集见https://github.com/ninglab/LIDDiA。
原文摘要 · Abstract (English)
Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks; however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDIA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDIA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDIA , demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it identifies one promising novel candidate on AR/NR3C4, a critical target for both prostate and breast cancers. Code and dataset are available at https://github.com/ninglab/LIDDiA
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