Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features-Reference-Cited by-同舟云学术

Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features

Published:2014 Issue:11 Volume:6 Page:1023-1033
ISSN:1757-9694
Container-title:Integr. Biol.
language:en
Short-container-title:Integr. Biol.

Author:

Ain Qurrat U.¹²³,Méndez-Lucio Oscar¹²³,Ciriano Isidro Cortés⁴⁵⁶⁷,Malliavin Thérèse⁴⁵⁶⁷,van Westen Gerard J. P.⁸⁹¹⁰^ORCID,Bender Andreas¹²³

Affiliation:

1. Centre for Molecular Informatics

2. Department of Chemistry

3. University of Cambridge, UK

4. Unité de Bioinformatique Structurale

5. Institut Pasteur and CNRS UMR 3825

6. Structural Biology and Chemistry Department

7. 75 724 Paris, France

8. European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI)

9. Wellcome Trust Genome Campus

10. Cambridge CB10 1SD, UK

Abstract

Predicting ligand selectivity of serine proteases by integrating biological and chemical similarity into proteochemometric modelling approaches.

Publisher

Oxford University Press (OUP)

Subject

Biochemistry,Biophysics

Link

http://pubs.rsc.org/en/content/articlepdf/2014/IB/C4IB00175C

Reference84 articles.

1. The druggable genome: an update

2. Global mapping of pharmacological space

3. Chemogenomic approaches to rational drug design

4. Chemoinformatics and Drug Discovery

5. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

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