DASPfind: new efficient method to predict drug–target interactions

Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://www.cbrc.kaust.edu.sa/daspfind.

Citation

DASPfind: new efficient method to predict drug–target interactions 2016, 8 (1) Journal of Cheminformatics

Acknowledgements

Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). The computational analysis for this study was performed on the Dragon and SnapDragon compute clusters of the Computational Bioscience Research Center at KAUST. The authors thank Valentin Rodionov (KAUST) for valuable discussion. We also thank Wenhui Wang (Case Western Reserve University) for help with HGBI and providing us with the dataset used in their study.

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[Dataset]
Ba-Alawi, W., Soufan, O., Magbubah Essack, Kalnis, P., & Bajic, V. (2016). DASPfind: new efficient method to predict drug–target interactions. Figshare. https://doi.org/10.6084/m9.figshare.c.3698116. DOI: 10.6084/m9.figshare.c.3698116 HANDLE: 10754/624146

Permanent link to this record

http://hdl.handle.net/10754/602276

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Applied Mathematics and Computational Science Program
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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