用深度强化学习设计新抗生素，100个候选物全部有效且广谱杀菌。
A deep reinforcement learning platform for antibiotic discovery
- 用64亿参数语言模型结合强化学习，从头生成抗菌肽。
- 100个设计肽均在体外显示纳摩尔级抑菌活性，99个具广谱杀菌能力。
- 可快速迭代优化，适合药物研发人员加速抗生素发现。
抗微生物耐药性（AMR）预计到2050年每年将导致多达1000万人死亡，亟需新型抗生素。本文提出ApexAmphion，一个基于深度学习的抗生素从头设计框架，融合了64亿参数的蛋白质语言模型与强化学习。模型首先在精选肽段数据上微调以捕捉抗菌序列规律，随后通过近端策略优化算法，在整合最小抑菌浓度（MIC）预测与可微分理化性质目标的复合奖励下进行优化。对100个设计肽的体外评估显示，所有候选物均表现出低MIC值（部分达纳摩尔级），100%命中率；其中99个对至少两种临床相关细菌具有广谱抗菌活性。先导分子主要通过强力靶向细胞质膜杀灭细菌。该方法将生成、评分与多目标优化统一于单一流程，可在数小时内快速产出多样且强效的候选分子，为肽类抗生素提供可扩展的设计路径，并支持快速迭代优化以提升活性与成药性。
原文摘要 · Abstract (English)
Antimicrobial resistance (AMR) is projected to cause up to 10 million deaths annually by 2050, underscoring the urgent need for new antibiotics. Here we present ApexAmphion, a deep-learning framework for de novo design of antibiotics that couples a 6.4-billion-parameter protein language model with reinforcement learning. The model is first fine-tuned on curated peptide data to capture antimicrobial sequence regularities, then optimised with proximal policy optimization against a composite reward that combines predictions from a learned minimum inhibitory concentration (MIC) classifier with differentiable physicochemical objectives. In vitro evaluation of 100 designed peptides showed low MIC values (nanomolar range in some cases) for all candidates (100% hit rate). Moreover, 99 our of 100 compounds exhibited broad-spectrum antimicrobial activity against at least two clinically relevant bacteria. The lead molecules killed bacteria primarily by potently targeting the cytoplasmic membrane. By unifying generation, scoring and multi-objective optimization with deep reinforcement learning in a single pipeline, our approach rapidly produces diverse, potent candidates, offering a scalable route to peptide antibiotics and a platform for iterative steering toward potency and developability within hours.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。