AI 自己改进找药流程，命中率翻倍
找新药通常靠专家定流程，AI 只是照着跑。这篇让 AI 连流程本身一起改：每次搜索的成败、反馈都存进记忆，下次自动调整策略和模型结构，越找越会找。在抗生素筛选上，它把命中率从专家流程的 8.7% 提到 15.9%;在肽段疫苗设计上，它找到 8 个实验验证的候选，而对照 AI 全军覆没。这不是明天就能用的药，但它是 AI 从「执行者」变成「自我优化的研究者」的实锤。
📄 原文摘要(英文)
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.