title: The role of different sampling methods in improving biological activity prediction using deep belief network.
abstract: Thousands of molecules and descriptors are available for a medicinal chemist thanks to the technological advancements in different branches of chemistry. This fact as well as the correlation between them has raised new problems in quantitative structure activity relationship studies. Proper parameter initialization in statistical modeling has merged as another challenge in recent years. Random selection of parameters leads to poor performance of deep neural network (DNN). In this research, deep belief network (DBN) was applied to initialize DNNs. DBN is composed of some stacks of restricted Boltzmann machine, an energy-based method that requires computing log likelihood gradient for all samples. Three different sampling approaches were suggested to solve this gradient. In this respect, the impact of DBN was applied based on the different sampling approaches mentioned above to initialize the DNN architecture in predicting biological activity of all fifteen Kaggle targets that contain more than 70k molecules. The same as other fields of processing research, the outputs of these models demonstrated significant superiority to that of DNN with random parameters. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Bioelectric and Biomedical engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar-Jerib Ave, Isfahan, 81746 73461, IR, Iran.
authors.forename: Fahimeh
authors.initials: F
authors.lastname: Ghasemi
authors.affiliation: Department of Medicinal Chemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Hezar-Jerib Ave, Isfahan, 81746 73461, IR, Iran.
authors.forename: Afshin
authors.initials: A
authors.lastname: Fassihi
authors.affiliation: Computer Science Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, E30107, Spain.
authors.forename: Horacio
authors.initials: H
authors.lastname: Pérez-Sánchez
authors.affiliation: Department of Bioelectric and Biomedical engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar-Jerib Ave, Isfahan, 81746 73461, IR, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Mehri Dehnavi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015195:Drug Design; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
keywords: biological activity prediction; deep belief network; deep neural network; initialization; statistical modeling