TY - GEN
T1 - Drug-Target Interactions Prediction Using Stacking Ensemble Learning Approach
AU - Prasetyo, Viko Pradana
AU - Anggraeni, Wiwik
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Accuracy
KW - Drug-Target Interaction
KW - Prediction
KW - SMOTE
KW - Stacking Ensemble Learning
UR - http://www.scopus.com/inward/record.url?scp=85204986828&partnerID=8YFLogxK
U2 - 10.1109/IES63037.2024.10665756
DO - 10.1109/IES63037.2024.10665756
M3 - Conference contribution
AN - SCOPUS:85204986828
T3 - 2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
SP - 681
EP - 686
BT - 2024 International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Ramadhani, Afifah Dwi
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Muhammad Rizani
A2 - Humaira, Fitrah Maharani
A2 - Nadziroh, Faridatun
A2 - Sa'adah, Nihayatus
A2 - Muna, Nailul
A2 - Rizki, Aris Bahari
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Electronics Symposium, IES 2024
Y2 - 6 August 2024 through 8 August 2024
ER -