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.
journal_name
Curr Protein Pept Scijournal_title
Current protein & peptide scienceauthors
Huang YA,You ZH,Chen Xdoi
10.2174/1389203718666161122103057subject
Has Abstractpub_date
2018-01-01 00:00:00pages
468-478issue
5eissn
1389-2037issn
1875-5550pii
CPPS-EPUB-79858journal_volume
19pub_type
杂志文章abstract::This article is a continuation of a series of reviews on the presence and the role of intrinsic disorder in milk proteins in the journal of Current Protein and Peptide Science. The focus of this article is on casein phosphopeptides, which are liberated during digestion of the milk protein casein. Structurally these ph...
journal_title:Current protein & peptide science
pub_type: 杂志文章,评审
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
pub_type: 杂志文章,评审
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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journal_title:Current protein & peptide science
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