| title | MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery |
|---|---|
| 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 |
| date | 2026-10-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.39714 |
| upvotes | 16 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf |
| cite | |
| 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. |
bibtex
@misc{zhang2026molrsi,
title={MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery},
author={Yikun Zhang and Xiwei Cheng and Mingze Yuan and Yu Zhang and Brian Shim and Aarti Krishnan and Daniel Rosen and Kunyang Sun and Yingze Wang and Zhenfeng Deng and Botao Yu and Bingxu Liu and David Liu and Tianyu Liu and Yuanqi Du and Nir Hacohen and James J. Collins and Wengong Jin},
year={2026},
eprint={2610.39714},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.39714}
}