Classification analysis using support vector machine, decision tree, and neural network with principal component analysis to determine molecular structure relationship from its biological activity on dipeptidyl peptidase IV inhibitors

Haris Hamzah, Alhadi Bustamam, Any Yanuar, Dewi Sarwinda

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

Abstract

A chronic metabolic disease that of ten affects adults is type 2 diabetes. Dipeptidyl peptidase-IV (DPP-IV) inhibitors are drug targets for diabetes mellitus type 2 (T2DM) that can block the enzyme dipeptidyl peptidase-IV. At this time, there are adverse effects from these inhibitors. Therefore, novel DPP-IV inhibitors are still expected with minimal adverse effects. In this paper, a machine learning approach is used to predict the molecular structure of DPP-IV inhibitors. There are 3363 inhibitors consisting of 1849 inhibitors with active labels and 1514 inhibitors with inactive labels that are optimized using fingerprint topology as descriptors. However, fingerprint topology always produces high-dimensional data. So, the principal component analysis method is proposed to reduce the dimension of the data set. Then, support vector machine, decision tree, and neural network are used for classifying DPP-IV inhibitors. The overall classification using the support vector machine method produces specificity, sensitivity, accuracy, and Matthews coefficient correlation C, respectively 0.774,0.826,0.803, and 0.604. These results indicate that the support vector machine method has a good ability in the classification of active and inactive DPP-IV inhibitors based on topological fingerprint as descriptors.

Original languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2020
EditorsBudi Purnama, Dewanta Arya Nugraha, Fuad Anwar
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440302
DOIs
Publication statusPublished - 16 Nov 2020
Event2020 International Conference on Science and Applied Science, ICSAS 2020 - Surakarta, Indonesia
Duration: 7 Jul 2020 → …

Publication series

NameAIP Conference Proceedings
Volume2296
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2020 International Conference on Science and Applied Science, ICSAS 2020
CountryIndonesia
CitySurakarta
Period7/07/20 → …

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