ILP based screening applied to predicting carbonic anhydrase II ligands

Tadasuke Ito, Masato Okada, Shotaro Togami, Shinya Ariyasu, Shin Aoki, Hayato Ohwada

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

Abstract

Machine learning has been often used for drug discovery in recent years. Inductive logic programming (ILP), that can express common features of each data in a qualitative, is one of the machine learning methods. The advantage of ILP is that the classification model is clear compared with other machine learning methods such as Support Vector Machine (SVM) because ILP classifies inhibitors using generated rules. ILP is allowed to learn structure of the compounds so that we can draw the common structure of the ligands from the generated rules. In this method, the data of ligands and decoys are collected from "A Database of Useful Decoys: Enhanced" (DUD-E). ILP provides classification model called a rule by learning these data. This study applies the ILP algorithm to the virtual screening of inhibitors of carbonic anhydrase II (CAH2). We demonstrate its performance by classifying ligands and decoys which aren't included in DUD-E. Our results show that ILP has the performance equivalent to SVM which is known for its high classification performance. In addition, this paper shows that ILP can derive rules explaining structural features of CAH2 ligands. Several of these rules are consistent with known property of CAH2 ligand. This paper demonstrate that ILP has high classification performance and clear classification model. Our method is useful for generating rules for ligand design.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages883-888
Number of pages6
ISBN (Electronic)9781467367981
DOIs
Publication statusPublished - 16 Dec 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: 9 Nov 201512 Nov 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/TerritoryUnited States
CityWashington
Period9/11/1512/11/15

Keywords

  • carbonic anhydrase
  • in silico screening
  • machine learning

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