Deep learning tools to accelerate antibiotic discovery

IntroductionAs 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 coveredThis 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 opinionAccurate 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.

Automated summary

As machine learning and artificial intelligence continue to expand in various sectors, including drug discovery, recent deep learning models have emerged as efficient tools to explore high-dimensional data and design compounds with desired properties. This article provides a review of key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. Various deep learning approaches, such as discriminative and generative models, are described and their integration with bioinformatics, molecular dynamics, and data augmentation is discussed as potential solutions to challenges in accurate antimicrobial prediction.