Survey of Similarity-Based Prediction of Drug-Protein Interactions

Current Medicinal Chemistry
Chen Wang, Lukasz Kurgan

Abstract

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source d...Continue Reading

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Citations

Jan 18, 2020·Briefings in Bioinformatics·Maryam BagherianKayvan Najarian
Dec 12, 2019·Frontiers in Chemistry·Maha ThafarVladimir B Bajic
May 23, 2020·International Journal of Molecular Sciences·Neann Mathai, Johannes Kirchmair
Aug 17, 2021·Artificial Intelligence Review·Suresh DaraMohamed Jawed Ahsan
Sep 24, 2021·Journal of Cheminformatics·Maha A ThafarXin Gao

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