Deep belief networks using hybrid fingerprint feature for virtual screening of drug design

Aries Fitriawan, Ito Wasito, Arida Ferti Syafiandini, Mukhlis Amien, Arry Yanuar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Virtual screening (VS) is a computational technique used in drug discovery. VS process usually works by identifying the ability of structures to bind each other. One of the structure interpretation is molecular fingerprints. Molecular fingerprints are used for computational drug discovery as feature for VS. A variety of fingerprint types has been introduced. Combining two or more fingerprints into a hybrid fingerprints has been found to improve the performance of VS. Furthermore, machine learning techniques have helped to improve the performance of VS. The purpose of this research is to find a new Deep Belief Networks (DBN) architecture approach for hybrid fingerprint features. In this paper, there were two different approaches for combining two fingerprints feature for DBN, then called initial combining and latter combining. This research used six protein target classes as same as the previous research about DBN for VS. The experiments result show that the best way to combine the fingerprints for DBN architecture is initial combining.

Original languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages417-420
Number of pages4
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
CountryIndonesia
CityMalang
Period15/10/1616/10/16

Keywords

  • Deep Belief Networks
  • drug design
  • hybrid fingerprint
  • molecular fingerprint
  • virtual screening

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  • Cite this

    Fitriawan, A., Wasito, I., Syafiandini, A. F., Amien, M., & Yanuar, A. (2017). Deep belief networks using hybrid fingerprint feature for virtual screening of drug design. In 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 (pp. 417-420). [7872737] (2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACSIS.2016.7872737