Literature DB >> 23177785

QSAR investigation of NaV1.7 active compounds using the SVM/Signature approach and the Bioclipse Modeling platform.

Ulf Norinder1, Maria E Ek.   

Abstract

A quantitative structure-activity relationship investigation of some NaV1.7 active compounds has been performed by repeated, random, external test set experiments employing structural descriptors (fingerprints) of signature type in combination with support vector machine (SVM) analysis using the radial basis function (RBF) kernel. The results from the investigation show remarkably stable performance from the derived in silico models in terms of statistical measures such as correlation coefficients as well as root mean squared errors (RMSEs) for the randomly selected external test sets. Also, the Bioclipse Modeling platform is utilized for introducing interpretation to the derived models.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 23177785     DOI: 10.1016/j.bmcl.2012.10.102

Source DB:  PubMed          Journal:  Bioorg Med Chem Lett        ISSN: 0960-894X            Impact factor:   2.823


  3 in total

1.  Large-scale ligand-based predictive modelling using support vector machines.

Authors:  Jonathan Alvarsson; Samuel Lampa; Wesley Schaal; Claes Andersson; Jarl E S Wikberg; Ola Spjuth
Journal:  J Cheminform       Date:  2016-08-10       Impact factor: 5.514

2.  The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.

Authors:  Egon L Willighagen; John W Mayfield; Jonathan Alvarsson; Arvid Berg; Lars Carlsson; Nina Jeliazkova; Stefan Kuhn; Tomáš Pluskal; Miquel Rojas-Chertó; Ola Spjuth; Gilleain Torrance; Chris T Evelo; Rajarshi Guha; Christoph Steinbeck
Journal:  J Cheminform       Date:  2017-06-06       Impact factor: 5.514

3.  Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

Authors:  Samuel Lampa; Jonathan Alvarsson; Ola Spjuth
Journal:  J Cheminform       Date:  2016-11-24       Impact factor: 5.514

  3 in total

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