Drug-Target Interactions Prediction Using Stacking Ensemble Learning Approach

Viko Pradana Prasetyo*, Wiwik Anggraeni

*Corresponding author for this work

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

Abstract

The process of drug discovery, particularly in the domain of drug-target interactions (DTI), is often time-consuming and costly, requiring extensive experimentation and validation before global approval can be obtained. To streamline this process and reduce associated costs, computational methods such as machine learning and deep learning are increasingly employed. However, these approaches often face challenges, including the need for large datasets, significant computational resources, and a tendency towards overfitting. Addressing these limitations, this study explores the use of Stacking Ensemble Learning (SEL) as a promising solution for DTI prediction. The proposed SEL model integrates multiple base learners, including Adaptive Boosting, Gradient Boosting, K-Nearest Neighbor, Random Forest, and Support Vector Machine, and enhances their performance through careful parameter tuning. To address data imbalances, the Synthetic Minority Oversampling Technique (SMOTE) is employed, ensuring more reliable predictions. The model was rigorously evaluated and demonstrated remarkable efficacy, achieving an accuracy of 99.047%. This research underscores the potential of the SEL approach in advancing computational drug discovery by enhancing prediction accuracy and robustness, offering a viable pathway for more efficient and cost-effective drug development processes.

Original languageEnglish
Title of host publication2024 International Electronics Symposium
Subtitle of host publicationShaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
EditorsAndhik Ampuh Yunanto, Afifah Dwi Ramadhani, Yanuar Risah Prayogi, Putu Agus Mahadi Putra, Weny Mistarika Rahmawati, Muhammad Rizani Rusli, Fitrah Maharani Humaira, Faridatun Nadziroh, Nihayatus Sa'adah, Nailul Muna, Aris Bahari Rizki
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages681-686
Number of pages6
ISBN (Electronic)9798350391992
DOIs
Publication statusPublished - 2024
Event26th International Electronics Symposium, IES 2024 - Denpasar, Indonesia
Duration: 6 Aug 20248 Aug 2024

Publication series

Name2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding

Conference

Conference26th International Electronics Symposium, IES 2024
Country/TerritoryIndonesia
CityDenpasar
Period6/08/248/08/24

Keywords

  • Accuracy
  • Drug-Target Interaction
  • Prediction
  • SMOTE
  • Stacking Ensemble Learning

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