The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design

Nadya Asanul Husna, Alhadi Bustamam, Arry Yanuar, Devvi Sarwinda, Oky Hermansyah

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

1 Citation (Scopus)

Abstract

Dipeptidyl peptidase-4 (DPP-4) inhibitor is an important target Diabetes Mellitus (DM) drug discovery. A quantitative Structure-activity Relationship (QSAR) model using molecular descriptors can be developed with the Machine Learning (ML) approach which Extreme Gradient Boosting (XGBoost) represents one of the most promising tools to establish it. The other tools that are used to construct the QSAR model are Support Vector Regressor (SVR) and Neural Network (NN), which the result obtained will be compared with XGBoost. The prediction results are comparable with the experimental value of the DPP4 inhibitor, in which the results reveal the superiority of the XGBoost over SVR and NN with the R-square value of XGBoost is 0.94.

Original languageEnglish
Title of host publicationSymposium on Biomathematics 2019, SYMOMATH 2019
EditorsMochamad Apri, Vitalii Akimenko
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420243
DOIs
Publication statusPublished - 22 Sep 2020
EventSymposium on Biomathematics 2019, SYMOMATH 2019 - Bali, Indonesia
Duration: 25 Aug 201928 Aug 2019

Publication series

NameAIP Conference Proceedings
Volume2264
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceSymposium on Biomathematics 2019, SYMOMATH 2019
CountryIndonesia
CityBali
Period25/08/1928/08/19

Keywords

  • DPP-IV
  • Neural networks (NN)
  • QSAR
  • Support vector regression (SVR)
  • XGBoost

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