Literature DB >> 30657882

SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server.

Lars Olsen1, Marco Montefiori1, Khanhvi Phuc Tran1, Flemming Steen Jørgensen1.   

Abstract

MOTIVATION: Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing.
RESULTS: SMARTCyp 3.0 is an updated version of our previous web server for prediction of site-of-metabolism for Cytochrome P450-mediated metabolism, now in Python 3 with increased structural coverage and new features. The SMARTCyp program is a first principle-based method using density functional theory determined activation energies for more than 250 molecules to identify the most likely site-of-metabolism. New features include a similarity measure between the query molecule and the model fragment, a new graphical interface and additional parameters expanding the structural coverage of the SMARTCyp program.
AVAILABILITY AND IMPLEMENTATION: The SMARTCyp server is freely available for use on the web at smartcyp.sund.ku.dk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2019        PMID: 30657882     DOI: 10.1093/bioinformatics/btz037

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  6 in total

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Review 3.  Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs.

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Journal:  Pharmaceuticals (Basel)       Date:  2021-05-17

4.  Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost.

Authors:  Peter C St John; Yanfei Guan; Yeonjoon Kim; Seonah Kim; Robert S Paton
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5.  Predicting reactivity to drug metabolism: beyond P450s-modelling FMOs and UGTs.

Authors:  Mario Öeren; Peter J Walton; Peter A Hunt; David J Ponting; Matthew D Segall
Journal:  J Comput Aided Mol Des       Date:  2020-06-12       Impact factor: 3.686

6.  Structure, In Vivo Detection, and Antibacterial Activity of Metabolites of SQ109, an Anti-Infective Drug Candidate.

Authors:  Satish R Malwal; Matthew D Zimmerman; Nadine Alvarez; Jansy P Sarathy; Véronique Dartois; Carol A Nacy; Eric Oldfield
Journal:  ACS Infect Dis       Date:  2021-07-19       Impact factor: 5.084

  6 in total

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