Prediction of Metabolism of Drugs Using Artificial Intelligence: How far have we Reached?

被引:24
|
作者
Kumar, Rajnish [1 ]
Sharma, Anju [1 ]
Siddiqui, Mohammed Haris [2 ]
Tiwari, Rajesh Kumar [1 ]
机构
[1] Amity Univ Uttar Pradesh, Amity Inst Biotechnol, Lucknow 226028, Uttar Pradesh, India
[2] Integral Univ, Dept Bioengn, PO Basha,Kursi Rd, Lucknow 226026, Uttar Pradesh, India
关键词
Artificial intelligence; drug designing; drug metabolism; machine learning; pharmacokinetics; prediction; MACHINE-LEARNING TECHNIQUES; CYP-MEDIATED SITES; CYTOCHROME-P450; 3A4; CHEMICAL METABOLISM; 1A2; INHIBITION; RS-PREDICTOR; MODELS; REGIOSELECTIVITY; CLASSIFICATION; STABILITY;
D O I
10.2174/1389200216666151103121352
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:129 / 141
页数:13
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