Deep belief networks for ligand-based virtual screening of drug design

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

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

4 Citations (Scopus)

Abstract

Virtual screening (VS) is a computational technique used in drug discovery. Virtual Screening process usually works by identifying structures that are most likely to bind the target of drug. Virtual screening is usually based on compound similarity or database docking. Thus, the identification for drug compounds based on structure classification still remain as a challenging task. The purpose of this research is to find a new approach for ligand-based virtual screening using machine learning technique. In this paper, the classification has been done by using Deep Belief Networks (DBN) method. The data from Nicotinamide Adenine Dinucleotide (NAD) protein target family were used for training and testing the model. This research used four protein target classes from literature and two protein target classes from DUD-E docking website. Feature were obtained from molecular fingerprint descriptor. The experiments result show that DBN method outperform the existing pharmacophore approach.

Original languageEnglish
Title of host publication2016 6th International Workshop on Computer Science and Engineering, WCSE 2016
PublisherInternational Workshop on Computer Science and Engineering (WCSE)
Pages655-659
Number of pages5
ISBN (Electronic)9789811100086
Publication statusPublished - 1 Jan 2016
Event2016 6th International Workshop on Computer Science and Engineering, WCSE 2016 - Tokyo, Japan
Duration: 17 Jun 201619 Jun 2016

Publication series

Name2016 6th International Workshop on Computer Science and Engineering, WCSE 2016

Conference

Conference2016 6th International Workshop on Computer Science and Engineering, WCSE 2016
CountryJapan
CityTokyo
Period17/06/1619/06/16

Keywords

  • Deep belief networks
  • Deep learning
  • Drug discovery
  • Virtual screening

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