MoLRSI recursively improves AI workflows for molecular discovery
Original title: MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery
MoLRSI optimizes workflows and models using persistent memory to recursively improve search policies in molecular discovery.
MoLRSI combines workflow optimization with cross-task memory for AI agents in molecular discovery. It uses feedback from performance, refinement and execution to improve both workflows and strategies. In antibiotic discovery it achieved a 15.9% hit rate balancing multiple objectives, outperforming a prior expert workflow. In peptide-MHC design it produced eight experimentally validated hits.
- Recursively improves search policy via experience
- 15.9% experimental hit rate in antibiotic discovery
- Eight validated hits against MART-1 and SARS-CoV-1
- Balances antibacterial activity and cytotoxicity
From the original article
1 min read at huggingface.co
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.
By Yikun Zhang, Xiwei Cheng, Mingze Yuan · huggingface.co
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