Comparison Accuracy of Multi-Layer Perceptron and DNN in QSAR Classification for Acetylcholinesterase Inhibitors

Mushliha, Alhadi Bustamam, Arry Yanuar, Wibowo Mangunwardoyo, Prasnurzaki Anki, Rizka Amalia

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

Abstract

Recently, the development of the artificial intelligence approach is a solution for evaluating the effectiveness, analysis, and safety of drug candidates due to a large number of data sets available. One of the approaches to artificial intelligence is deep learning. Deep learning has a significant influence on drug discovery procedures for rational drug development and optimization so that it can affect public health. The discovery of various inhibitors needs reliable models to figure out the side effects of the drug without requiring large costs and long amounts of time. A new way for the treatment of Alzheimer's disease is Acetylcholinesterase inhibitors. The Quantitative Structure-Activity Relationship (QSAR) model is a model used to filter large databases of the compound to figure the biological properties of chemical molecules based on their structure. The modeling that was used in this study was QSAR classification. The QSAR classification model predicted active and inactive compounds in Acetylcholinesterase inhibitors. There were 3809 inhibitors which consisted of 2215 active inhibitors and 1594 inactive inhibitors. They were labeled using fingerprints as descriptors. This study compared the performances of MLP and DNN in the classification. The result of this study showed DNN had better accuracy of 0.841 in classification.

Original languageEnglish
Title of host publicationAIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665424820
DOIs
Publication statusPublished - 28 Apr 2021
Event2021 International Conference on Artificial Intelligence and Mechatronics Systems, AIMS 2021 - Virtual, Bandung, Indonesia
Duration: 28 Apr 202130 Apr 2021

Publication series

NameAIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems

Conference

Conference2021 International Conference on Artificial Intelligence and Mechatronics Systems, AIMS 2021
Country/TerritoryIndonesia
CityVirtual, Bandung
Period28/04/2130/04/21

Keywords

  • Acetylcholinesterase inhibitor
  • Alzheimer's disease
  • Deep Learning
  • DNN
  • MLP
  • QSAR

Fingerprint

Dive into the research topics of 'Comparison Accuracy of Multi-Layer Perceptron and DNN in QSAR Classification for Acetylcholinesterase Inhibitors'. Together they form a unique fingerprint.

Cite this