Application of Machine Learning in Pharmacometabolomics

Machine learning can effectively solve the prediction problems existing in drug metabolism research in the human body. As a powerful tool, it has been widely used in pharmacometabolomics. Research on the application of machine learning in pharmacometabolomics uses a variety of algorithms and techniques. In terms of feature extraction, researchers rely on the structural characteristics, chemical properties, functional groups, etc. of drug molecules to describe the metabolic characteristics of the drug. Model optimization and performance evaluation are performed by learning from known drug-metabolizing enzyme interaction data and using a training and validation process. Machine learning has achieved remarkable research results in pharmacometabolomics. The accuracy of the machine learning algorithm in this article is up to 98%. Metabolic pathway prediction can accurately determine the metabolic pathways and metabolite structures of drugs, thereby revealing the metabolic dynamics of drugs.