RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information

Author(s): Lei Wang, Zhu-Hong You*, Xing Chen*, Xin Yan, Gang Liu, Wei Zhang

Journal Name: Current Protein & Peptide Science

Volume 19 , Issue 5 , 2018

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Graphical Abstract:


Background: Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI).

Methods: In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments.

Results: Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods.

Conclusions: Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

Keywords: Target interactions, position-specific scoring matrix, auto covariance, rotation forest, support vector machine, drug substructure fingerprint.

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Article Details

Year: 2018
Published on: 22 March, 2018
Page: [445 - 454]
Pages: 10
DOI: 10.2174/1389203718666161114111656
Price: $65

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