LIDDIA: Language-based Intelligent Drug Discovery Agent
This addresses the slow and expensive drug discovery process for pharmaceutical researchers, offering a low-cost and adaptable tool, though it appears incremental as it builds on existing AI methods for chemistry.
The paper tackles the problem of automating drug discovery by introducing LIDDIA, an autonomous agent that uses large language models to navigate the process, generating molecules meeting key criteria for over 70% of 30 clinically relevant targets and identifying a promising novel candidate for a critical cancer target.
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