AI agent improves drug discovery workflows across tasks
MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery
Artificial Intelligence
Summary
Discovering new medicines involves searching through many possible molecules to find ones that work well and are safe. The authors introduce MoLRSI, an AI agent that learns from past experiences to improve how it searches for promising molecules over time. It not only automates experiments but also optimizes its methods and models to do better in future searches. In tests on antibiotic development and peptide design, MoLRSI found more effective molecules than prior expert methods and other AI approaches.
What this means in practice
- •For pharmaceutical research teams: Create and improve automated molecular screening processes that balance multiple biological objectives to find better drug candidates.
- •For biotech software developers: Build AI-driven platforms that iteratively refine molecule design workflows by learning from prior screening results to enhance discovery efficiency.
Authors
Yikun Zhang, Xiwei Cheng, Mingze Yuan, Yu Zhang, Brian Shim, Aarti Krishnan, Daniel Rosen, Kunyang Sun, Yingze Wang, Zhenfeng Deng, Botao Yu, Bingxu Liu, David Liu, Tianyu Liu, Yuanqi Du, Nir Hacohen, James J. Collins, Wengong Jin
Abstract
Molecular discovery requires navigating vast combinatorial search spaces while balancing competing biological objectives. AI agents can automate expert-defined molecular design protocols, but automation alone does not address how to optimize workflows for new tasks or improve their development through experience. Here we introduce MoLRSI, an AI agent that combines systematic optimization of workflows and model architectures with persistent cross-task memory to recursively improve its search policy for molecular discovery. Workflow performance, refinement outcomes, and execution feedback guide subsequent searches, allowing the agent to improve both its workflows and its strategy for developing them. We evaluate MoLRSI in two challenging molecular discovery settings that require balancing competing properties. In antibiotic discovery, MoLRSI developed a screening workflow balancing antibacterial activity, cytotoxicity and structural novelty, achieving an experimental hit rate of 15.9%, compared with 8.7% for a previous expert-led workflow using the same training data and screening library. In de novo peptide-MHC binder design, MoLRSI developed workflows that jointly consider target binding and specificity, producing eight experimentally validated hits against MART-1 and SARS-CoV-1 pMHCs with preferential binding over tested off-targets while Claude Science completely failed. These findings demonstrate how experience-guided workflow optimization can address competing discovery objectives while improving the search strategies used in subsequent campaigns.