A deep reinforcement learning platform for antibiotic discovery

• URL: http://arxiv.org/abs/2509.18153v1
• Date: Tue, 16 Sep 2025 18:21:42 GMT
• Title: A deep reinforcement learning platform for antibiotic discovery
• Authors: Hanqun Cao, Marcelo D. T. Torres, Jingjie Zhang, Zijun Gao, Fang Wu, Chunbin Gu, Jure Leskovec, Yejin Choi, Cesar de la Fuente-Nunez, Guangyong Chen, Pheng-Ann Heng,
• Abstract summary: Antimicrobial resistance (AMR) is projected to cause up to 10 million deaths annually by 2050.<n>We present ApexAmphion, a deep-learning framework for de novo design of antibiotics.
• Score: 101.30486136547285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification [2.327827051373412]
  We present ESCAPE, an experimental framework integrating over 80.000 peptides from 27 validated repositories.<n>Our dataset separates antimicrobial peptides from negative sequences and incorporates their functional annotations into a biologically coherent multilabel hierarchy.<n>Building on ESCAPE, we propose a transformer-based model that leverages sequence and structural information to predict multiple functional activities of peptides.
  arXiv  Detail & Related papers  (2025-11-06T21:10:48Z)
• AI-guided Antibiotic Discovery Pipeline from Target Selection to Compound Identification [0.0]
  Recent advances in protein structure prediction and machine learning offer a promising opportunity to accelerate drug discovery.<n>We develop an end-to-end, guided realization realization of 3D-structure-aware generative models.<n>This work provides a comparative benchmark and blueprint for deploying artificial intelligence in early-stage antibiotic development.
  arXiv  Detail & Related papers  (2025-04-15T11:36:27Z)
• xTrimoABFold: De novo Antibody Structure Prediction without MSA [77.47606749555686]
  We develop a novel model named xTrimoABFold to predict antibody structure from antibody sequence. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss.
  arXiv  Detail & Related papers  (2022-11-30T09:26:08Z)
• Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection [57.131117785001194]
  Large-scale screening of new AMP candidates is expensive, time-consuming, and now affordable in developing countries. We propose a novel active machine learning-based framework that statistically minimizes the number of wet-lab experiments needed to design new AMPs.
  arXiv  Detail & Related papers  (2022-09-18T14:30:48Z)
• AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation [53.43922443725598]
  We present AntBO: a Combinatorial optimisation algorithm enabling efficient in silico design of the CDRH3 region. To benchmark AntBO, we use the Absolut! software suite as a black-box oracle because it can score the target specificity and affinity of designed antibodies in silico. In under 200 protein designs, AntBO can suggest antibody sequences that outperform the best binding sequence drawn from 6.9 million experimentally obtained CDRH3s.
  arXiv  Detail & Related papers  (2022-01-29T12:03:04Z)
• HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides [5.61222966894307]
  We build a diverse and comprehensive multi-label protein sequence database by collecting and cleaning amino acids from various AMP databases. We develop an end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, it further predicts what targets the AMP can effectively kill from eleven available classes.
  arXiv  Detail & Related papers  (2021-11-11T02:10:07Z)
• PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models [2.0750380105212116]
  With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. We propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets.
  arXiv  Detail & Related papers  (2020-05-27T11:30:15Z)
• Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics [109.70543391923344]
  CLaSS (Controlled Latent attribute Space Sampling) is an efficient computational method for attribute-controlled generation of molecules. We screen the generated molecules for additional key attributes by using deep learning classifiers in conjunction with novel features derived from atomistic simulations. The proposed approach is demonstrated for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency.
  arXiv  Detail & Related papers  (2020-05-22T15:57:58Z)
• CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models [74.58583689523999]
  We propose an end-to-end framework, named CogMol, for designing new drug-like small molecules targeting novel viral proteins. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme. CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity.
  arXiv  Detail & Related papers  (2020-04-02T18:17:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.