Computational methods for prediction of drug properties - application to Cytochrome P450 metabolism prediction

被引：1

作者：

Patrascu, Mihai Burai ^{[1
]}

Plescia, Jessica ^{[1
]}

Kalgutkar, Amit ^{[2
]}

Mascitti, Vincent ^{[2
]}

Moitessier, Nicolas ^{[1
]}

机构：

[1] McGill Univ, Dept Chem, 801 Sherbrooke St West, Montreal, PQ H3A 0B8, Canada

[2] Pfizer Inc, Med Design, 610 Main St, Cambridge, MA 02139 USA

来源：

ARKIVOC | 2019年

基金：

加拿大自然科学与工程研究理事会;

关键词：

Sites of metabolism; machine learning; quantum mechanics; structure-based drug design; ligand-based drug design; SEMIEMPIRICAL METHODS; RS-PREDICTOR; SITES; DOCKING; REGIOSELECTIVITY; SMARTCYP; MODELS; OPTIMIZATION; VALIDATION; PARAMETERS;

D O I：

10.24820/ark.5550190.p010.970

中图分类号：

O62 [有机化学];

学科分类号：

070303 ; 081704 ;

摘要：

Computational methods are becoming essential in the drug discovery world. Structure-based methods (i.e. docking), ligand-based methods, and machine learning are common practice. In this review, we present the major methods and their application to the prediction of cytochrome P450 (CYP)-mediated drug metabolism. More specifically, this mini-review is focused on the different methods used in predicting sites of metabolism (SoMs), and presents the advantages and disadvantages of various SoM prediction tools that are currently in use in both academia and industry. [GRAPHICS] .

引用

页码：280 / 298

页数：19

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