[2603.27303] Self-evolving AI agents for protein discovery and directed evolution

Computer Science > Artificial Intelligence arXiv:2603.27303 (cs) [Submitted on 28 Mar 2026] Title:Self-evolving AI agents for protein discovery and directed evolution Authors:Yang Tan, Lingrong Zhang, Mingchen Li, Yuanxi Yu, Bozitao Zhong, Bingxin Zhou, Nanqing Dong, Liang Hong View a PDF of the paper titled Self-evolving AI agents for protein discovery and directed evolution, by Yang Tan and 7 other authors View PDF Abstract:Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFactory2 provides an autonomous framework that shifts from static tool usage to dynamic workflow synthesis via a self-evolving multi-agent infrastructure to address protein-related demands. It outperforms a set of well-known agents on the VenusAgentEval benchmark, and autonomously organizes the discovery and optimization of proteins from a single natural language prompt. Comments: Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Quantitative Methods (q-bio.QM) Cite as: arXiv:2603.27303 [cs.AI]   (or arXiv:2603.27303v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.27303 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yang Tan [view email] [v1] Sat, 28 Mar 2026 15:16:49 UTC (14,575 KB) Full-text links: Access Paper: View a PDF of the paper titled Self-evolving AI agents for protein discovery and directed evolu...