Deep learning tools to accelerate antibiotic discovery

ABSTRACTIntroduction As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity.Areas covered This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics.Expert opinion Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.KEYWORDS: Drug discoverydrug designantimicrobialsDeep-learning modelsinfectious diseases Article highlights AI and ML provide innovative ways to expedite antibiotic discovery by optimizing molecular design.The successful use of discriminative and generative deep learning models is directly impacted by the algorithm and influences the model´s ability to represent diverse molecular structures.Discriminative deep learning models are computational frameworks for predicting antibiotic activity, leveraging their specific architectures and approaches.Generative deep learning models are applied in antibiotic design utilizing the composition of molecules to generate potent drug candidates.Deep learning models yet face challenges due to data quality and availability limitations.AcknowledgmentsThe authors thank Dr. Karen Pepper for editing the manuscript and de la Fuente Lab members for insightful discussions. All figures were prepared in BioRender.com. Molecules shown in the review paper were rendered using the PyMOL Molecular Graphics System, Version 2.5.2 Schrödinger, LLC.Declaration of interestC de la Fuente-Nunez provides consulting services to Invaio Sciences and is a member of the Scientific Advisory Boards of Nowture S.L. and Phare Bio. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Reviewer disclosuresPeer reviewers on this manuscript have no relevant financial or other relationships to disclose.Additional informationFundingCesar de la Fuente-Nunez holds a Presidential Professorship at the University of Pennsylvania, is a recipient of the Langer Prize by the AIChE Foundation, and acknowledges funding from the IADR Innovation in Oral Care Award, the Procter & Gamble Company, United Therapeutics, a BBRF Young Investigator Grant, the Nemirovsky Prize, Penn Health-Tech Accelerator Award, the Dean's Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania, the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM138201, and the Defense Threat Reduction Agency (DTRA; HDTRA11810041, HDTRA1-21-1-0014, and HDTRA1-23-1-0001).