Enzyme classification on dud-e database using logistic regression ensemble (Lorens)

Heri Kuswanto*, Jainap N. Melasasi, Hayatso Ohwada

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Discovery of drugs has been a complex process, time-consuming and expensive until an alternative of making drug has been found i.e. using in silico method to discover potential inhibitor. During the process of drug design, compound classification is carried out through docking score steps. The aim of this research is to predict the docking score results using proper methods for classification i.e. a computationally based method and a standard statistical method. This research examined three target enzymes listed in DUD-E database i.e. aofb, cah2 and hs90a. Each enzyme consists of different compounds that will be classified as good inhibitor (ligand) and bad inhibitor (decoy). In this research, the docking score step is conducted by binary logistic regression and logistic regression ensemble (Lorens). Binary logistic regression yields on 90.4% of accuracy for aofb, 91.7% for cah2 and 94% for hs90a enzyme. Meanwhile, logistic regression ensemble (Lorens) results on the accuracy levels of 88.95, 92.1 and 100% for aofb, cah2 and hs90a consecutively. This paper showed that logistic regression ensemble method outperforms standard logistic regression to be used for the inhibitor classification.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
EditorsIvan Zelinka, Pandian Vasant, Vo Hoang Duy, Tran Trong Dao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages93-109
Number of pages17
ISBN (Print)9783319669830
DOIs
Publication statusPublished - 2018
Event1st International Conference on the Computer Science and Engineering, COMPSE 2016 - Penang, Malaysia
Duration: 11 Nov 201612 Nov 2016

Publication series

NameStudies in Computational Intelligence
Volume741
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference1st International Conference on the Computer Science and Engineering, COMPSE 2016
Country/TerritoryMalaysia
CityPenang
Period11/11/1612/11/16

Keywords

  • Classification
  • Ensemble
  • Enzyme
  • Lorens

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