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A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences
Huang, YA (Huang, Yu-An); You, ZH (You, Zhu-Hong); Chen, X (Chen, Xing)
2018
Source PublicationCURRENT PROTEIN & PEPTIDE SCIENCE
ISSN1389-2037
Volume19Issue:5Pages:468-478
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. Method: 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. Conclusion: 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.

KeywordDrug-target Interactions Pseudo Substitution Matrix Representation Drug Substructure Fingerprint Extremely Randomized Trees Computational Model
DOI10.2174/1389203718666161122103057
Indexed BySCI
WOS IDWOS:000428417500005
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5297
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)
Affiliation1.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China
Recommended Citation
GB/T 7714
Huang, YA ,You, ZH ,Chen, X . A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences[J]. CURRENT PROTEIN & PEPTIDE SCIENCE,2018,19(5):468-478.
APA Huang, YA ,You, ZH ,&Chen, X .(2018).A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.CURRENT PROTEIN & PEPTIDE SCIENCE,19(5),468-478.
MLA Huang, YA ,et al."A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences".CURRENT PROTEIN & PEPTIDE SCIENCE 19.5(2018):468-478.
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