iDTi-CSsmoteB: Identification of Drug-Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE

Identifying interaction between drug and protein is a crucial challenge in drug discovery, which can lead the researchers to develop novel drug compounds or new target proteins for the existing drugs. The determination of drug-target interactions (DTIs) is an extremely time-consuming, costly, and te...

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Published in:IEEE access 2019, Vol.7, p.48699-48714
Main Authors: Mahmud, S. M. Hasan, Chen, Wenyu, Jahan, Hosney, Liu, Yongsheng, Sujan, Nasir Islam, Ahmed, Saeed
Format: Article
Language:English
Subjects:
Algorithms
AM-PseAAC
Chemicals
Classifiers
Datasets
DP-PseAAC
drug-target interactions
Drugs
Feature extraction
Ion channels
Molecular structure
molecular substructure fingerprint
over-sampling SMOTE
Predictive models
Protein sequence
Proteins
PSSM-Bigram
Sampling methods
Substructures
Target recognition
XGBoost classifier
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creator Mahmud, S. M. Hasan
Chen, Wenyu
Jahan, Hosney
Liu, Yongsheng
Sujan, Nasir Islam
Ahmed, Saeed
description Identifying interaction between drug and protein is a crucial challenge in drug discovery, which can lead the researchers to develop novel drug compounds or new target proteins for the existing drugs. The determination of drug-target interactions (DTIs) is an extremely time-consuming, costly, and tedious task with wet-lab experiments. To date, multiple computational techniques have been presented to simplify the drug discovery process, but a huge number of interactions are still undiscovered. Furthermore, a class imbalance is a critical challenge regarding this experiment which can significantly degrade the classification accuracy that has not been effectively addressed yet. In this paper, we proposed a novel high-throughput computational model, called iDTi-CSsmoteB, for identification of DTIs based on drug chemical structures and protein sequences. More specifically, the protein sequence is extracted through position-specific scoring matrix (PSSM)-Bigram, amphiphilic pseudo amino acid composition (AM-PseAAC) and dipeptide PseAAC descriptors which represents evolutionary and sequence information. The drug chemical structure is represented as a molecular substructure fingerprint (MSF) which describes the existence of the functional fragments or groups. Finally, we used the over-sampling SMOTE technique to overcome the imbalance issue of the datasets and applied XGBoost algorithm as a classifier to predict DTIs. To evaluate the performance of iDTi-CSsmoteB, several experiments have been conducted on four benchmark datasets, namely, enzyme, ion channel, GPCR, and nuclear receptor based on fivefold cross validation. The experimental analysis exhibits that our model outperforms similar methods in terms of area under the ROC (auROC) curve. In addition, our achieved results indicate the effectiveness of the feature extraction techniques, balancing methods, and classifier for predicting the DTIs which can provide substance for new drug development. iDTi-CSsmoteB webserver is available online at http://idticssmoteb-uestc.me/ .
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source IEEE Open Access Journals
subjects Algorithms
AM-PseAAC
Chemicals
Classifiers
Datasets
DP-PseAAC
drug-target interactions
Drugs
Feature extraction
Ion channels
Molecular structure
molecular substructure fingerprint
over-sampling SMOTE
Predictive models
Protein sequence
Proteins
PSSM-Bigram
Sampling methods
Substructures
Target recognition
XGBoost classifier
title iDTi-CSsmoteB: Identification of Drug-Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE
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