LIDDIA: Language-based Intelligent Drug Discovery Agent

TL;DR

LIDDIA is an AI-powered autonomous agent that leverages large language models to navigate the drug discovery process in silico, generating promising molecules and identifying novel candidates efficiently.

Contribution

This paper introduces LIDDIA, a novel language-based autonomous agent that enhances in silico drug discovery by combining reasoning capabilities with exploration-exploitation strategies.

Findings

LIDDIA generates molecules meeting key criteria on over 70% of targets.

It balances exploration and exploitation effectively in chemical space.

It identified a promising novel candidate for a critical cancer target.

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