title: A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.
abstract: BACKGROUND
  Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient.
  
  
  OBJECTIVE
  Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information.
  
  
  METHODS
  More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor.
  
  
  RESULTS
  The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases.
  
  
  CONCLUSIONS
  The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features.
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong.
authors.forename: Yu-An
authors.initials: YA
authors.lastname: Huang
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
mesh_terms: D003198:Computer Simulation; D030562:Databases, Protein; D066264:Datasets as Topic; D055808:Drug Discovery; D008958:Models, Molecular; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D013329:Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins
keywords: Drug-target interactions; computational model; drug substructure fingerprint; extremely
  randomized trees; pseudo substitution matrix representation