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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/31734254
Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting - PubMed Skip to main page content
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. 2020 Jan 15:589:113507.
doi: 10.1016/j.ab.2019.113507. Epub 2019 Nov 15.

Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting

S M Hasan Mahmud  1 , Wenyu Chen  2 , Han Meng  3 , Hosney Jahan  4 , Yongsheng Liu  5 , S M Mamun Hasan  6
Affiliations

Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting

S M Hasan Mahmud et al. Anal Biochem. .

Abstract

Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/.

Keywords: Data imbalance; Drug-target interaction; Feature extraction; Feature selection; Molecular substructure fingerprint; XGBoost classifier.

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