International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Driven Drug Repurposing: Unlocking New Therapeutics for Existing Drugs |
|---|---|
| ABSTRACT | Drug repurposing offers a cost-effective and time-efficient strategy to explore fresh therapeutic roles for established drugs. This project proposes an AI-driven analytical framework that leverages a blend of deep learning tools to analyze large-scale biomedical data, including molecular features, drug–target interactions, and disease profiles. By identifying hidden patterns and predicting novel drug–disease associations, the system generates high-confidence repurposing candidates. The approach enhances accuracy, reduces research time, and provides explainable insights that can support further experimental validation. This project demonstrates how AI can accelerate the discovery of new therapeutics from already approved drugs. |
| AUTHOR | MADHURI DEEKSHITH S, AISHWARYA U P, PRAVEEN KUMAR J S, SHAHID HABEEB KHAN, SNEHA S K Project Guide, Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, India Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312101 |
| pdf/101_AI Driven Drug Repurposing Unlocking New Therapeutics for Existing Drugs.pdf | |
| KEYWORDS | |
| References | [1] Utku Kose ,Omer Deperlioglu,(2024)“Combining Deep Learning Models for Improved Drug Repurposing: Advancements and an Extended Solution Methodology”,2024. [2] Gharizadeh A., Abbasi K., Ghareyazi A., et al.(2024) “HGTDR: Heterogeneous Graph Transformer for Drug Repurposing” [3] Davide Bacciu., Alessio Gravina.(2024) –“Deep Graph Networks for Drug Repurposing with Multi Protien-targets” [4] Wang Q., Zhang X., Wu T.J., et al. (2021) – “COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation” [5] Sherine Glory J., Dr P Durga Devi.(2022) –“Impact of Machine and Deep Learning in Drug Repurposing” [6] Zeng X., Zhu S., Liu X., Zhou Y., Nussinov R., Cheng F. (2021) – “DeepDR:A Network-Based Deep Learning Approach to In Silico Drug Repositioning”. [7] Sadegh S., Arman S., Golkar S., et al. (2022)–“Machine Learning Approaches for Drug Repurposing in Neurodegenerative Diseases” [8] Rojas et al., (2022) –“ AI-Driven Drug Repurposing in Infectious Diseases: A Systematic Review” [9] Scherman and Fetro (2020) "Drug repositioning for rare diseases: Knowledge-based success stories" [10] Roessler et al. (2021) "Drug Repurposing for Rare Diseases" |
