Human opinion inspired feature selection strategy for predicting the pleasantness of a molecule

Ritesh Kumar, Rishemjit Kaur, Amol P. Bhondekar, Gajendra P.S. Raghava

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

Abstract

The identification of features responsible for smell of a molecule has been a long-standing challenge. We use cheminformatics and opinion dynamics based optimization algorithm to identify feature subsets of a molecule, which can predict how pleasant a molecule will smell. We have also compared it to standard feature selection techniques. The features identified reveal that three classes of features are primarily responsible for pleasantness. The work may open up some innovative inroads into feature identification and their physical understanding into the olfactory stimulus-percept problem.

Original languageEnglish
Title of host publicationAdvanced Computational and Communication Paradigms - Proceedings of International Conference on ICACCP 2017
EditorsNabendu Chaki, Siddhartha Bhattacharyya, Debanjan Konar, Udit Kr. Chakraborty, Chingtham Tejbanta Singh
PublisherSpringer Verlag
Pages197-205
Number of pages9
ISBN (Print)9789811082368
DOIs
StatePublished - 1 Jan 2018
Externally publishedYes
Event1st International Conference on Advanced Computational and Communication Paradigms, ICACCP 2017 - Majitar, India
Duration: 8 Sep 201710 Sep 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume706
ISSN (Print)2194-5357

Conference

Conference1st International Conference on Advanced Computational and Communication Paradigms, ICACCP 2017
Country/TerritoryIndia
CityMajitar
Period8/09/1710/09/17

Keywords

  • CODO
  • F-test
  • Feature subset selection
  • Olfaction
  • Opinion dynamics optimizer
  • Pleasantness
  • Wrapper

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science

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