Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Automated summary

Drug metabolism and excretion are crucial in determining drug efficacy and safety. Artificial intelligence (AI) has emerged as a powerful tool for predicting these processes, offering potential for faster drug development and improved success rates. This review highlights recent advancements in AI-based prediction of drug metabolism and excretion, including deep learning and machine learning algorithms. It also provides a list of public data sources and prediction tools, discusses challenges in AI model development, and explores future perspectives in the field.