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
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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.
| Item Type: | Article |
|---|---|
| Subjects: | Information Technology (IT) ISI-indexed journals |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Thanh Hải Đặng |
| Date Deposited: | 05 Jan 2018 04:08 |
| Last Modified: | 05 Jan 2018 04:08 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/2903 |
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- Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry. (deposited 05 Jan 2018 04:08) [Currently Displayed]
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