Abstract
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.
Highlights
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning
The framework consists of five main parts: representing each DTI as a vector based on various biological information, selecting feature subsets of DTIs, constructing strong Negative DTI Samples (NDTISs), computing the similarity weights of the ambiguous examples, and building an support vector machine (SVM)-based optimization model
(1) We compared the performances of our proposed NDTISE method with random selection method and NCPIS on a DTI data provided by NCPIS26
Summary
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. Ligand-based methods[7] might be limited when target proteins have no known association information[9], while molecular docking methods[8] are computationally costly and depend largely on the 3D structures of target proteins[3, 9] To overcome these problems, multiple computational models have been increasingly exploited to determine potential DTIs10–12. A kernel regression-based approach[17] was proposed to predict possible DTIs from human enzymes, ion channels, GPCRs and nuclear receptors by integrating the chemical structures of drug compounds, sequence information of target proteins and known DTI networks into a unified framework. A Random Forest (RF)-based learning approach[23] was exploited to predict DTIs by integrating substructures of compounds, physicochemical and biomedical properties of proteins and known DTI networks This approach cannot detect possible DTIs for a new drug or target without association information. Multiscale feature representation approach[24] based on deep learning, random projection ensemble method[25] and support vector machine (SVM)[12] were utilized to infer DTI candidates for new drugs or targets
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