DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.

Sk Mazharul Islam,Sumanta Ray,Sk Md Mosaddek Hossain,Alberto Fernández-Hilario

doi:10.1371/journal.pone.0246920

Abstract

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

Highlights

Drug development strategies, known as drug repositioning or drug repurposing or drug reprofiling, predict the interaction among drugs and targets from the existing drug-target databases [1]
There are two critical issues in this domain: a massive amount of drugs and targets creating a vast search space and highly imbalanced drugtarget interactions dataset as there is a tiny number of drug-target interactions unveiled so far
The size of the negative samples is much larger than the size of the positive samples

Summary

Introduction

Known as drug repositioning or drug repurposing or drug reprofiling, predict the interaction among drugs and targets from the existing drug-target databases [1]. There are two types of drug-target interaction: competitive inhibitors and allosteric inhibitors. Competitive inhibitors adhere to the target’s active site to suppress reactions. The research and findings of compounds’ properties, their reactions/responses to drugs, and targets have generated large, complex databases that need efficient computational methods to analyze and predict drug-target interaction. Due to known side effects and easier government approval, drug-repurposing facilitate pharmaceutical companies to launch existing authorized drugs and compounds in the market for new therapeutic purposes [4]. Drug repositioning usually reinvestigates existing drugs which were denied approval due to new therapeutic indications

Methods

Results

Conclusion