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Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry

[ Vol. 17 , Issue. 30 ]


Hai Pham-The, Nguyen-Hai Nam, Doan-Viet Nga, Dang Thanh Hai, Karel Dieguez-Santana, Yovani Marrero-Ponce, Juan A. Castillo-Garit, Gerardo M. Casanola-Martin* and Huong Le-Thi-Thu*   Pages 3269 - 3288 ( 20 )


Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of 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 this paper, we presented 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 illustrated 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.


Histone Deacetylase (HDAC) inhibitors, Quantitative Structure –Activity Relationships (QSAR), Multiple classifier system, Ensemble design, Artificial neural network, Histone deacetylase.


Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi, University of Engineering and Technology, Vietnam National University, 144 Xuan Thuy, Hanoi, Faculty of Life Sciences, Amazonian State University, Puyo, Pastaza, Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA),Escuela de Medicina, Edificio de Especialidades Medicas, Quito, Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas “Dr. Serafín Ruiz de Zarate Ruiz” de Villa Clara, Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi

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