arXiv cs.AI· 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·· 3 小时前AI 评分57
MoLRSI 论文提出递归自改进 AI 智能体用于分子发现，实验命中率 15.9%
MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery
AI 导读
论文提出 MoLRSI，一种结合工作流系统优化与跨任务持久记忆的递归自改进 AI 智能体，用于分子发现。在抗生素发现中，它开发的筛选工作流平衡抗菌活性、细胞毒性和结构新颖性，实验命中率 15.9%，高于此前专家工作流的 8.7%；在 MART-1 和 SARS-CoV-1 pMHC 结合剂从头设计中，产出 8 个经实验验证的命中，而 Claude Science 完全失败。
正文 · 原文
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.
| Comments: | 69 pages,8 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39714 [cs.AI] |
| (or arXiv:2610.39714v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39714 arXiv-issued DOI via DataCite |
Submission history
From: Yikun Zhang [view email]
[v1]
Thu, 29 Oct 2026 09:46:50 UTC (18,501 KB)
来源：arXiv cs.AI · arxiv.org