Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79105
Title: Predicting drug-target interactions based on small positive samples
Authors: Hu, P 
Chan, KCC 
Hu, Y 
Keywords: Computational methods
Drug discovery
Drug target interaction (DTI)
One-class classification
Positive samples
Protein and compound representations
Issue Date: 2018
Publisher: Bentham Science Publishers
Source: Current protein & peptide science, 2018, v. 19, no. 5, p. 479-487 How to cite?
Journal: Current protein & peptide science 
Abstract: Background: A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable.
Objective: Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs.
Method: In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a one-class classification algorithm to build a prediction model based only on known positive interactions.
Results: We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset.
Conclusion: Performance evaluation of ODT shows that it can be potentially useful. It confirms that predicting potential or missing DTIs based on the known interactions is a promising direction to solve problems related to the use of uncertain and unreliable negative samples and those related to the great demand in computational resources.
URI: http://hdl.handle.net/10397/79105
ISSN: 1389-2037
EISSN: 1875-5550
DOI: 10.2174/1389203718666161108102330
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