title: Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?
abstract: Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.
authors.affiliation: Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow-226028, Uttar Pradesh, India. rkumar2@lko.amity.edu.
authors.forename: Rajnish
authors.initials: R
authors.lastname: Kumar
authors.forename: Anju
authors.initials: A
authors.lastname: Sharma
authors.forename: Mohammed Haris
authors.initials: MH
authors.lastname: Siddiqui
authors.forename: Rajesh Kumar
authors.initials: RK
authors.lastname: Tiwari
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D006801:Humans; D008658:Inactivation, Metabolic / Q000502:physiology*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000378:metabolism*
chemical_list: D004364:Pharmaceutical Preparations