Abstract
Search of valid drug candidates for a given target is a vital part of modern drug discovery. Since the problem was established, a number of approaches have been proposed that augment interaction networks with, typically, two compound/target similarity networks. In this work we propose a method capable of using an arbitrary number of similarity or interaction networks. We adapt an existing method for random walks on heterogeneous networks and show that adding additional networks improves prediction quality.
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Notes
- 1.
An open-access database is available at http://prosite.expasy.org.
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in ligands.csv, interactions.csv, and targets_and_families.csv, respectively.
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Cutoff proposed by researchers from CERMN (http://cermn.unicaen.fr).
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Global Query Cross-Database Search System gene identifiers: https://www.ncbi.nlm.nih.gov/gene.
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Precision at 20.
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Koptelov, M., Zimmermann, A., Crémilleux, B. (2018). Link Prediction in Multi-layer Networks and Its Application to Drug Design. In: Duivesteijn, W., Siebes, A., Ukkonen, A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science(), vol 11191. Springer, Cham. https://doi.org/10.1007/978-3-030-01768-2_15
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