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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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Review Article

A Comparative Analytical Review on Machine Learning Methods in Drugtarget Interactions Prediction

Author(s): Zahra Nikraftar and Mohammad Reza Keyvanpour*

Volume 19, Issue 5, 2023

Published on: 03 February, 2023

Page: [325 - 355] Pages: 31

DOI: 10.2174/1573409919666230111164340

Price: $65

Abstract

Background: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predicting drug-target interactions have been introduced successfully to solve these problems and have received extensive attention.

Objective: In this paper, we provided a summary of databases that are useful in DTI prediction and intend to concentrate on machine learning methods as a chemogenomic approach in drug discovery. Unlike previous surveys, we propose a comparative analytical framework based on the evaluation criteria.

Methods: In our suggested framework, there are three stages to follow: First, we present a comprehensive categorization of machine learning-based techniques as a chemogenomic approach for drug-target interaction prediction problems; Second, to evaluate the proposed classification, several general criteria are provided; Third, unlike other surveys, according to the evaluation criteria introduced in the previous stage, a comparative analytical evaluation is performed for each approach.

Results: This systematic research covers the earliest, most recent, and outstanding techniques in the DTI prediction problem and identifies the advantages and weaknesses of each approach separately. Additionally, it can be helpful in the effective selection and improvement of DTI prediction techniques, which is the main superiority of the proposed framework.

Conclusion: This paper gives a thorough overview to serve as a guide and reference for other researchers by providing an analytical framework which can help to select, compare, and improve DTI prediction methods.

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