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Molecules 2017, 22(7), 1119; doi:10.3390/molecules22071119

Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures

1
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China
2
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
3
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 21116, China
*
Authors to whom correspondence should be addressed.
Received: 27 May 2017 / Revised: 27 June 2017 / Accepted: 3 July 2017 / Published: 5 July 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks. View Full-Text
Keywords: DTI; RVM; BIGP; PCA DTI; RVM; BIGP; PCA
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Meng, F.-R.; You, Z.-H.; Chen, X.; Zhou, Y.; An, J.-Y. Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures. Molecules 2017, 22, 1119.

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