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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Chronological Order Based Wrapper Technique for Drug-Target Interaction Prediction (CO-WT DTI)

Author(s): Kavipriya Gananathan*, Manjula Dhanabalachandran and Vijayan Sugumaran

Volume 17, Issue 6, 2022

Published on: 05 July, 2022

Page: [541 - 557] Pages: 17

DOI: 10.2174/1574893617666220509185052

Price: $65

Abstract

Background: Drug-Target Interactions (DTIs) are used to suggest new medications for diseases or reuse existing drugs to treat other diseases since experimental procedures take years to complete, and FDA (Food and Drug Administration) permission is necessary for drugs to be made available in the market.

Objective: Computational methods are favoured over wet-lab experiments in drug analysis, considering that the process is tedious, time-consuming, and costly. The interactions between drug targets are computationally identified, paving the way for unknown drug-target interactions for numerous diseases unknown to researchers.

Methods: This paper presents a Chronological Order-based Wrapper Technique for Drug-Target Interaction prediction (CO-WT DTI) to discover novel DTI. In our proposed approach, drug features, as well as protein features, are obtained by three feature extraction techniques while dimensionality reduction is implemented to remove unfavourable features. The imbalance issue is taken care of by balancing methods while the performance of the proposed approach is validated on benchmark datasets.

Results: The proposed approach has been validated using four broadly used benchmark datasets, namely, GPCR (G protein-coupled receptors), enzymes, nuclear receptors, and ion channels. Our experimental results outperform other state-of-the-art methods based on the AUC (area under the Receiver Operating Characteristic (ROC) curve) metric, and Leave-One-Out Cross-Validation (LOOCV) is used to evaluate the prediction performance of the proposed approach.

Conclusion: The performance of feature extraction, balancing methods, dimensionality reduction, and classifier suggests ways to contribute data to the development of new drugs. It is anticipated that our model will help refine ensuing explorations, especially in the drug-target interaction domain.

Keywords: LPC, drug-target interactions, MSF, over-sampling SMOTE, random under-sampling, k-separated-bigrams PSSM, AADP-PSSM, XGBoost classifier.

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