title: RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.
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.
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.|College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: School of Foreign Languages, Zaozhuang University, Zaozhuang, 277100, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Yan
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Liu
authors.affiliation: College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
mesh_terms: D003198:Computer Simulation; D030562:Databases, Protein; D066264:Datasets as Topic; D008958:Models, Molecular; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry*; D013329:Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins
keywords: Target interactions; auto covariance; drug
  substructure fingerprint; position-specific scoring matrix; rotation forest; support vector machine