Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.

Abstract

Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL) techniques, have introduced innovative approaches to metabolism research, enabling more accurate predictions and insights. This paper emphasizes computational and AI-driven methodologies, highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models (GEMs) at the omics level. Challenges still remain, including data heterogeneity and model interpretability. This work aims to provide valuable insights and references for researchers in drug discovery and development, ultimately contributing to the advancement of precision medicine.