In silico approaches and tools for the prediction of drug metabolism and fate: A review

  • Kazmi, Sayada Reemsha; 
  • Jun, Ren; 
  • Yu, Myeong-Sang; 
  • Jung, Chanjin; 
  • Na, Dokyun
Citations

WEB OF SCIENCE

90
Citations

SCOPUS

115

초록

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.

키워드

In silico tools; Toxicity prediction; Metabolism prediction; drug discovery; drug metabolism; MACHINE LEARNING TECHNIQUES; MOLECULAR DOCKING; PROTEIN FLEXIBILITY; GENETIC ALGORITHM; QSAR; TOXICITY; BINDING; INHIBITORS; DISCOVERY; VITRO
제목
In silico approaches and tools for the prediction of drug metabolism and fate: A review
저자
Kazmi, Sayada Reemsha; Jun, Ren; Yu, Myeong-Sang; Jung, Chanjin; Na, Dokyun
DOI
10.1016/j.compbiomed.2019.01.008
발행일
2019-03
유형
Review
저널명
Computers in Biology and Medicine
권
106
페이지
54 ~ 64