Abstract
Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/.
Highlights
Increased the redundancy problem of the compound names or the gene names and has been the main obstacle for literature based systematic text mining methods
We have presented iDTI-ESBoost, a novel method to predict and identify drug-target interactions. iDTI-ESBoost is unique in its exploitation of structural features along with the evolutionary features to predict drug-protein interactions
On four benchmark datasets known as the gold standard data in the literature, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under Receiver Operating Characteristic curve
Summary
Increased the redundancy problem of the compound names or the gene names and has been the main obstacle for literature based systematic text mining methods. Since the three dimensional native structure of most of the protein targets are not available, most of the supervised learning methods in the literature do not exploit the structure based features. Huang et al.[32] used extremely randomized trees model and represented the proteins as pseudo substitution matrix generated from its amino acid sequence information and the drugs as moelcular fingerprint In another recent work, Wang et al.[33] explored PSSM based features and drug fingerprints and used rotation forest based predictor. Our proposed method uses a novel set of features extracted using structural information along with the evolutionary features and molecular fingerprints of drugs. The rest of the paper is organised as follows which is suggested in[44]: description of dataset, formulation of statistical samples, selection and development of a powerful classification algorithm, demonstration of the performance of the predictor using cross-validation, implementation of web server followed by a conclusion
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