IMR Press / FBL / Volume 26 / Issue 7 / DOI: 10.52586/4936
Open Access Original Research
Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern
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1 School of Information Engineering, Xijing University, 710123 Xi’an, Shaanxi, China
*Correspondence: (Wen-Zhun Huang)
Front. Biosci. (Landmark Ed) 2021, 26(7), 222–234;
Submitted: 25 May 2021 | Revised: 21 June 2021 | Accepted: 2 July 2021 | Published: 30 July 2021
Copyright: © 2021 The Author(s). Published by BRI.
This is an open access article under the CC BY 4.0 license (

Introduction: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments and clinical trials is still extremely complex, laborious, and costly. As a result, a robust computer-aided model is of an urgent need to predict drug-target interactions (DTIs). Methods: In this paper, we report a novel computational approach combining fuzzy local ternary pattern (FLTP), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) to identify DTIs. More specially, the target primary sequence is first numerically characterized into PSSM which records the biological evolution information. Afterward, the FLTP method is applied in extracting the highly representative descriptors of PSSM, and the combinations of FLTP descriptors and drug molecular fingerprints are regarded as the complete features of drug-target pairs. Results: Finally, the entire features are fed into rotation forests for inferring potential DTIs. The experiments of 5-fold cross-validation (CV) achieve mean accuracies of 89.08%, 86.14%, 82.41%, and 78.40% on Enzyme, Ion Channel, GPCRs, and Nuclear Receptor datasets. Discussion: For further validating the model performance, we performed experiments with the state-of-art support vector machine (SVM) and light gradient boosting machine (LGBM). The experimental results indicate the superiorities of the proposed model in effectively and reliably detect potential DTIs. There is an anticipation that the proposed model can establish a feasible and convenient tool to identify high-throughput identification of DTIs.

Drug-target interactions
Fuzzy local ternary pattern
Drug molecular fingerprints
Rotation forest
Fig. 1.
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