Abstract
Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.
Highlights
Detecting the interactions between compounds and proteins is one of the most active parts of the genomic drug development field, as it plays a critical role during the discovery of novel drug candidates [1]
We present a computational approach to identify potential drug–target interactions (DTIs) based on the information of chemical fingerprints and target protein sequences
To verify the performance of the dual-tree complex wavelet transform (DTCWT) descriptor, we compared it with the local phase quantization (LPQ) method
Summary
Detecting the interactions between compounds (drugs, molecules, ligands) and proteins (targets) is one of the most active parts of the genomic drug development field, as it plays a critical role during the discovery of novel drug candidates [1]. Over the past few years, numerous experimental approaches have been introduced to identify DTIs, but few of them have been tested and detected as interactive [5,6] These traditional experimental-based methods need to address the problem of high false-positive and false-negative rates [7]. For these reasons, there was a strong demand for the development of novel computational approaches to shorten the drug development cycle and reduce the time taken to detect drug–target pairs [8]
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