TITLE:
AI-Driven Drug Repurposing for Oncology: Identifying Novel Cancer Treatments from Existing Drugs
AUTHORS:
Ashok Ghimire, Pankajkumar Tejraj Jain
KEYWORDS:
AI-Driven Drug Repurposing, Oncology, Machine Learning, Cancer Treatments, FDA-Approved Drugs, Drug Resistance, Tumor Heterogeneity, Personalized Medicine, Computational Models, Genomics, Clinical Data, Drug Discovery, Treatment Optimization, Molecular Analysis
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.13 No.5,
May
26,
2025
ABSTRACT: AI-driven drug repurposing is shaking things up in the world of oncology. It uses artificial intelligence (AI) to find new cancer treatments from drugs that are already approved by the FDA. This innovative approach taps into machine learning algorithms, extensive biological datasets, and sophisticated computational models to uncover potential new uses for medications that were initially created for different health issues. In oncology, where options can be limited due to challenges like drug resistance and the diversity of tumors, AI has the power to reveal fresh, effective treatment possibilities by sifting through complex molecular, genomic, and clinical data. This speedy identification of promising drug candidates can greatly cut down on the time, costs, and risks typically associated with traditional drug development. This paper dives into how AI is being applied in drug repurposing for cancer treatment, showcasing recent achievements, the merging of AI with clinical data, and the potential for tailored treatment strategies. It also looks at the hurdles in data integration, model validation, and getting regulatory approval, while pointing out future paths for the field.