Machine Learning Based Drug Repurposing Strategies for Predicting Drug-Target Interactions

• Author(s): G. Selvakumar ; Dr. G. Rajendran
• Paper ID: 1708262
• Page: 144-153
• Published Date: 06-05-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 11 May-2025

Abstract

The process of discovering and developing a drug is not at all a routine but rather a time consuming and complex process. A large number of potential components of a drug may have the possibility to get rejected on the ground of toxicity. Drug repositioning is a very important aspect of drug discovery; it refers to the identification of new targets for already existing or abandoned drugs. Computational prediction of the binding affinity between the chemical compounds and the protein targets has a major role in reducing the need for wet-lab analysis on large scales, hence improving the chances of identification of lead compounds. Even more recently, ML and deep learning methods have been used for predicting drug-target interactions, which has effectively reduced much time and cost that went with the effort of drug discovery. Proteins that drugs target fall into four big categories: enzymes, ion channels, G-protein-coupled receptors, and nuclear receptors. Principles of drug repurposing broadly fall into two categories: drug-based and disease-based. This analysis will give whether a single drug can treat multiple diseases in resemblance for drug-based repurposing. On the other hand, in the disease-based repurposing, new applications are sought from existing drugs in known targets. Several in-silico methodologies have been widely explored, especially the machine learning approaches, for predicting potential drug-target interactions. This work, therefore, aims at reviewing some of the methodologies of drug repurposing and their applications in the process of drug discovery and development using machine learning models. With the vast availability of biological data and computational resources for researchers, the aim would be the exploitation of these tools to further facilitate the process of drug discovery.

Keywords

Machine Learning, Deep Learning, Drug Discovery, Drug Repurposing, Drug-Target Interaction Prediction

Citations

IRE Journals:
G. Selvakumar , Dr. G. Rajendran "Machine Learning Based Drug Repurposing Strategies for Predicting Drug-Target Interactions" Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 144-153

IEEE:
G. Selvakumar , Dr. G. Rajendran "Machine Learning Based Drug Repurposing Strategies for Predicting Drug-Target Interactions" Iconic Research And Engineering Journals, 8(11)