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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Research Article

Large-scale Prediction of Drug-Protein Interactions Based on Network Information

Author(s): Xinsheng Li, Daichuan Ma*, Yan Ren, Jiesi Luo and Yizhou Li

Volume 18, Issue 1, 2022

Published on: 15 March, 2021

Page: [64 - 72] Pages: 9

DOI: 10.2174/1573409917666210315094213

Abstract

Background: The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs.

Methods: In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs.

Results: The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test.

Conclusion: Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Keywords: Drug discovery, recommender system, bipartite graph, drug-protein interaction, collaborative filtering, Jaccard index.

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