A deep reinforcement learning platform for antibiotic discovery
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912599924277248 |
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| author | Cao, Hanqun Torres, Marcelo D. T. Zhang, Jingjie Gao, Zijun Wu, Fang Gu, Chunbin Leskovec, Jure Choi, Yejin de la Fuente-Nunez, Cesar Chen, Guangyong Heng, Pheng-Ann |
| author_facet | Cao, Hanqun Torres, Marcelo D. T. Zhang, Jingjie Gao, Zijun Wu, Fang Gu, Chunbin Leskovec, Jure Choi, Yejin de la Fuente-Nunez, Cesar Chen, Guangyong Heng, Pheng-Ann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A deep reinforcement learning platform for antibiotic discovery Cao, Hanqun Torres, Marcelo D. T. Zhang, Jingjie Gao, Zijun Wu, Fang Gu, Chunbin Leskovec, Jure Choi, Yejin de la Fuente-Nunez, Cesar Chen, Guangyong Heng, Pheng-Ann Machine Learning Biomolecules 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. |
| title | A deep reinforcement learning platform for antibiotic discovery |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2509.18153 |