Next Article in Journal
Isopentyl-Sulfide-Impregnated Nano-MnO2 for the Selective Sorption of Pd(II) from the Leaching Liquor of Ores
Next Article in Special Issue
Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks
Previous Article in Journal
Anti-Inflammatory and Anti-Urolithiasis Effects of Polyphenolic Compounds from Quercus gilva Blume
Previous Article in Special Issue
Recent Advances in Conotoxin Classification by Using Machine Learning Methods
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Molecules 2017, 22(7), 1119;

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

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
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)
Full-Text   |   PDF [798 KB, uploaded 7 July 2017]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top