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Article

Predicting Drug–Target Interactions Based on the Ensemble Models of Multiple Feature Pairs

by 1, 2, 2,* and 1,3,4,*
1
Department of Computer Science & Technology, Tongji University, Shanghai 201804, China
2
Institutes of Physical Science and Information Technology & School of Internet, Anhui University, Hefei 230601, China
3
School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243032, China
4
Key Laboratory of Power Electronics and Motion Control Anhui Education Department, Ma’anshan 243032, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Irina Moreira
Int. J. Mol. Sci. 2021, 22(12), 6598; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22126598
Received: 15 May 2021 / Revised: 9 June 2021 / Accepted: 16 June 2021 / Published: 20 June 2021
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
Backgroud: The prediction of drug–target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The prediction method based on the Ensemble model of Multiple Feature Pairs (Ensemble-MFP) is introduced. Firstly, three negative sets are generated according to the Euclidean distance of three feature pairs. Then, the negative samples of the validation set/test set are randomly selected from the union set of the three negative sets in the validation set/test set. At the same time, the ensemble model with weight is optimized and applied to the test set. Results: The area under the receiver operating characteristic curve (area under ROC, AUC) in three out of four sub-datasets in gold standard datasets was more than 94.0% in the prediction of new drugs. The effectiveness of the proposed method is also shown with the comparison of state-of-the-art methods and demonstration of predicted drug–target pairs. Conclusion: The Ensemble-MFP can weigh the existing feature pairs and has a good prediction effect for general prediction on new drugs. View Full-Text
Keywords: drug–target interactions; ensemble model of Multiple Feature Pairs (Ensemble-MFP); model weight sum; support vector machines drug–target interactions; ensemble model of Multiple Feature Pairs (Ensemble-MFP); model weight sum; support vector machines
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MDPI and ACS Style

Wang, C.; Zhang, J.; Chen, P.; Wang, B. Predicting Drug–Target Interactions Based on the Ensemble Models of Multiple Feature Pairs. Int. J. Mol. Sci. 2021, 22, 6598. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22126598

AMA Style

Wang C, Zhang J, Chen P, Wang B. Predicting Drug–Target Interactions Based on the Ensemble Models of Multiple Feature Pairs. International Journal of Molecular Sciences. 2021; 22(12):6598. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22126598

Chicago/Turabian Style

Wang, Cheng, Jun Zhang, Peng Chen, and Bing Wang. 2021. "Predicting Drug–Target Interactions Based on the Ensemble Models of Multiple Feature Pairs" International Journal of Molecular Sciences 22, no. 12: 6598. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22126598

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