Kumar Rajnish, Sharma Anju, Siddiqui Mohammed Haris, Tiwari Rajesh Kumar
Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow-226028, Uttar Pradesh, India.
Curr Drug Metab. 2016;17(2):129-41. doi: 10.2174/1389200216666151103121352.
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.
药物代谢信息是药物研发的重要组成部分。对药物代谢进行建模需要确定所涉及的酶、代谢速率和程度、代谢部位等。人们一直在不断尝试利用人工智能预测药物代谢，以降低进入临床前和临床试验的候选药物的损耗率。目前，有许多使用支持向量机、人工神经网络、贝叶斯分类器等的代谢预测模型。迫切需要回顾它们到目前为止的进展，并解决代谢预测中存在的挑战。在这次尝试中，我们展示了目前可用的文献模型以及一些关于药物代谢预测的关键问题。
