eprintid: 2903
rev_number: 5
eprint_status: archive
userid: 327
dir: disk0/00/00/29/03
datestamp: 2018-01-05 04:08:34
lastmod: 2018-01-05 04:08:34
status_changed: 2018-01-05 04:08:34
type: article
succeeds: 2784
metadata_visibility: show
creators_name: Pham, The Hai/V
creators_name: Nguyen, Hai Nam
creators_name: Doan, Viet Nga
creators_name: Dang, Thanh Hai
creators_name: Dieguez-Santana, Karel
creators_name: Marrero-Poncee, Yovani
creators_name: Castillo-Garit, Juan/A
creators_name: Casanola-Martin, Gerardo/M
creators_name: Le, Thi Thu Huong
creators_id: hai.dang@vnu.edu.vn
title: Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry
ispublished: pub
subjects: IT
subjects: isi
divisions: fac_fit
abstract: Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of data on chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In the present paper, we present MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrate our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models.
date: 2017
date_type: published
publisher: Bentham Science
official_url: https://www.ncbi.nlm.nih.gov/pubmed/29231145
id_number: 10.2174/1568026618666171212111018
full_text_status: none
publication: Current Topics in Medicinal Chemistry
volume: 18
pagerange: 1-20
refereed: TRUE
issn: 1873-4294
citation:   Pham, The Hai/V and Nguyen, Hai Nam and Doan, Viet Nga and Dang, Thanh Hai and Dieguez-Santana, Karel and Marrero-Poncee, Yovani and Castillo-Garit, Juan/A and Casanola-Martin, Gerardo/M and Le, Thi Thu Huong  (2017) Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry.  Current Topics in Medicinal Chemistry, 18 .   pp. 1-20.  ISSN 1873-4294