[论文解读] LIDDIA: Language-based Intelligent Drug Discovery Agent

Reza Averly, Frazier N. Baker+2 more|ArXiv.org|Feb 19, 2025

Scientific Computing and Data Management被引用 3

一句话总结

tldr: 本文提出 LIDDIA，一种用于体外前临床药物发现的自治 LLM 驱动代理，能够跨多个靶点生成并优化新颖药物候选分子，输出高质量、具有多样性的结果，且靶点成功率强。

ABSTRACT

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

研究动机与目标

• Motivate the need for autonomous, intelligent routing of the drug discovery workflow.
• 提出一种模块化代理架构，将推理、执行、评估与记忆与 grounding 工具结合起来。
• Demonstrate that the agent generates high-quality, novel molecules across multiple targets and properties.
• Show that the agent balances exploration and exploitation and identifies promising candidates for key targets like EGFR.

提出的方法

• Introduce a four-component architecture: Reasoner, Executor, Evaluator, and Memory.
• Ground Reasoner and Evaluator in Claude 3.5/related LLMs with task-specific prompts.
• Use Pocket2Mol for generation, GraphGA for optimization, and a screening processor for selection.
• Evaluate molecules on QED, Lipinski’s Rule of Five (LRF), SAS, Vina affinity (VNA), and novelty (NVT).
• Measure Diversity (DVS) and Target Success Rate (TSR) across 30 protein targets with known structures and drugs.

实验结果

研究问题

• RQ1 Can an autonomous LLM-based agent generate diverse, high-quality drug candidates across multiple targets?
• RQ2 How well does the agent balance exploration and exploitation in chemical space to improve hit identification and lead optimization?
• RQ3 Is the agent capable of identifying promising candidates for clinically relevant targets (e.g., EGFR) with favorable property profiles?

主要发现

• LIDDIA 在 30 个靶点的 TSR 达到 73.3%，超越基线方法。
• 平均而言，每个靶点生成分子中有 85% 的分子在关键性质上为高质量（HQ）。
• LIDDIA 生成的分子显示出高新颖性和强的 VNA（结合亲和力）特征，在 VNA 方面超越基线。
• LIDDIA 将从头生成与迭代优化相结合，提供在 QED、LRF、SAS 与 VNA 上均为 HQ 的分子。
• 关于 EGFR 的案例研究显示出具有与已批准药物相当的特性配置的有前景候选分子。