Literature DB >> 30682640

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

Sayada Reemsha Kazmi1, Ren Jun1, Myeong-Sang Yu1, Chanjin Jung1, Dokyun Na2.   

Abstract

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.
Copyright © 2019. Published by Elsevier Ltd.

Keywords:  In silico tools; Metabolism prediction; Toxicity prediction; drug discovery; drug metabolism

Mesh:

Substances:

Year:  2019        PMID: 30682640     DOI: 10.1016/j.compbiomed.2019.01.008

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  7 in total

1.  Metabolite Structure Assignment Using In Silico NMR Techniques.

Authors:  Susanta Das; Arthur S Edison; Kenneth M Merz
Journal:  Anal Chem       Date:  2020-07-15       Impact factor: 6.986

2.  Machine learning models in the prediction of drug metabolism: challenges and future perspectives.

Authors:  Eleni E Litsa; Payel Das; Lydia E Kavraki
Journal:  Expert Opin Drug Metab Toxicol       Date:  2021-11-02       Impact factor: 4.481

3.  Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape.

Authors:  Anne Marie E Smith; Kiril Lanevskij; Andrius Sazonovas; Jesse Harris
Journal:  Front Toxicol       Date:  2022-06-21

Review 4.  Predicting epitopes for vaccine development using bioinformatics tools.

Authors:  Valentina Yurina; Oktavia Rahayu Adianingsih
Journal:  Ther Adv Vaccines Immunother       Date:  2022-05-21

5.  Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.

Authors:  Daniel E Dawson; Brandall L Ingle; Katherine A Phillips; John W Nichols; John F Wambaugh; Rogelio Tornero-Velez
Journal:  Environ Sci Technol       Date:  2021-04-15       Impact factor: 9.028

6.  Prediction of drug metabolites using neural machine translation.

Authors:  Eleni E Litsa; Payel Das; Lydia E Kavraki
Journal:  Chem Sci       Date:  2020-09-24       Impact factor: 9.825

7.  Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body.

Authors:  Dmitry A Filimonov; Anastassia V Rudik; Alexander V Dmitriev; Vladimir V Poroikov
Journal:  Int J Mol Sci       Date:  2020-10-11       Impact factor: 5.923

  7 in total

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