• Ekansh Jadon

The process of drug discovery and repurposing has been revolutionized by advancements in machine learning techniques. This research explores the application of machine learning models in predicting drug-target interactions, identifying off-target effects, and suggesting potential repurposing opportunities for existing drugs. The study utilizes large-scale biological and chemical datasets to train and validate various machine learning models, demonstrating significant improvements in accuracy and efficiency over traditional methods. The results highlight the potential of machine learning in accelerating the drug discovery process and reducing associated costs.

Drug Discovery, Drug Repurposing, Machine Learning, Drug-Target Interaction, Off-Target Effects, Biological Datasets

"Drug Discovery and Repurposing: Advancements in Machine Learning for Accelerating Drug Discovery and Repurposing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.h172-h174, July-2024, Available :http://www.jetir.org/papers/JETIR2407723.pdf

"Drug Discovery and Repurposing: Advancements in Machine Learning for Accelerating Drug Discovery and Repurposing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pph172-h174, July-2024, Available at : http://www.jetir.org/papers/JETIR2407723.pdf