English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Predicting the target-drug interactions (DITs) is of great important for screening new drug candidate and understanding biological processes. However, identifying the drug-target interactions through traditional experiments is still costly, laborious and complicated. Thus, there is a great need for developing reliable computational methods to effectively predict DTIs. In this study, we report a novel computational method combining local optimal oriented pattern (LOOP), Position Specific Scoring Matrix (PSSM) and Rotation Forest (RF) for predicting DTI. Specifically, the target protein sequence is firstly transformed as the PSSM, in which the evolutionary information of protein is retained. Then, the LOOP is used to extract the feature vectors from PSSM, and the sub-structure information of drug molecule is represented as fingerprint features. Finally, RF classifier is adopted to infer the potential drug-target interactions. When the experiment is carried out on four benchmark datasets including <italic>enzyme</italic>, <italic>ion channel</italic>, <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> <italic>protein-coupled receptors (GPCRs)</italic>, and <italic>nuclear receptor</italic>, we achieved the high average prediction accuracies of 89.09&#x0025;, 87.53&#x0025;, 82.05&#x0025;, and 73.33&#x0025; respectively. For further evaluating the proposed method, we compare the prediction performance of the proposed method with the state-of-the-art support vector machine (SVM) and K-Nearest Neighbor (KNN). The comprehensive experimental results illustrate that the proposed method is reliable and efficiency for predicting DTIs. It is anticipated that the proposed method can become a useful tool for predicting a large-scale potential DTIs.

Prediction of Drug-Target Interactions by Ensemble Learning Method From Protein Sequence and Drug Fingerprint