Analysis of Proteasome Inhibition Prediction Using Atom-Based Quadratic Indices Enhanced by Machine Learning Classification Techniques

ISSN: 1875-628X (Online)
ISSN: 1570-1808 (Print)


Volume 11, 10 Issues, 2014


Download PDF Flyer




Letters in Drug Design & Discovery

Aims & ScopeAbstracted/Indexed in


Submit Abstracts Online Submit Manuscripts Online

Editor-in-Chief:
Atta-ur-Rahman, FRS
Honorary Life Fellow
Kings College
University of Cambridge
Cambridge
UK
Email: lddd@benthamscience.org

View Full Editorial Board

Subscribe Purchase Articles Order Reprints

Current: 0.961
5 - Year: 0.917

Analysis of Proteasome Inhibition Prediction Using Atom-Based Quadratic Indices Enhanced by Machine Learning Classification Techniques

Author(s): Gerardo M. Casanola-Martin, Huong Le-Thi-Thu, Yovani Marrero-Ponce, Juan A Castillo- Garit, Francisco Torrens, Facundo Perez-Gimenez and Concepcion Abad

Affiliation: Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100 Burjassot, Spain.

Abstract

In this work the use of 2D atom-based quadratic indices is shown in the prediction of proteasome inhibition. Machine learning approaches such as support vector machine, artificial neural network, random forest and k-nearest neighbor were used as main techniques to carry out two quantitative structure-activity relationship (QSAR) studies. First, a database consisting of active and non-active classes was predicted with model performances above 85% and 80% in learning and test series, respectively. Second a regression-based model was developed which allow to estimate the EC50 with Q2 values of 52.89 and 50.19, in training and prediction sets, respectively, were developed. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures.




Keywords: Atom-based quadratic index, Classification and regression model, Machine learning, Proteasome inhibition, QSAR, TOMOCOMD-CARDD software.

Purchase Online Order Reprints Order Eprints Rights and Permissions

  
  



Article Details

Volume: 11
Issue Number: 6
First Page: 705
Last Page: 711
Page Count: 7
DOI: 10.2174/1570180811666140122001144
Advertisement

Related Journals




Webmaster Contact: urooj@benthamscience.org Copyright © 2014 Bentham Science