IMCCRT 2025 - 4538 Title - Ai-Driven Drug Repurposing: Uncovering Hidden Potentials Of Established

Title - Ai-Driven Drug Repurposing: Uncovering Hidden Potentials Of Established Medications For Rare Disease Treatment

Author's Name - Rakesh Paul

Abstract The purpose of this research is to investigate whether AI-based methods can identify additional therapeutic applications of existing medicines specific to rare conditions. Patients with rare diseases face several issues, mainly because there are few therapeutic options for such ailments. Due to this problem, drug repurposing, a process whereby known drugs are searched for new uses, proved to be a feasible approach to meeting these needs. It means that when AI is used in drug repurposing, the chance of identifying potential candidates and achieving positive results will be much faster. In this study, the pharmacology profile of existing drugs and mechanisms of action and safety data of drugs in the dataset were included. Several DL and NLP techniques were applied in this context to develop models for predicting latent drug-disease pairs regarding rare disorders. Commercialized AI formulations were built upon past correlations between drugs and diseases, whereby new candidates could be identified for certain rare diseases that had not been considered in the past. Furthermore, external validation of the entire list of predicted drug disease pairs was done experimentally and by literature curation. This study shows that using AI for drug repurposing might prove highly useful in finding the right treatment for rare diseases. This study shows that the machine learning approach works well, as seven out of the ten identified compounds were proven to be definite drugs that could be used for rare disorders’ treatment. The machine learning approach is efficient in that it helps to seek for clues among millions of compounds. The current work highlights the importance of future clinical research of the identified drug-disease pairs as well as the further development of new approaches to the treatment of rare disease patients.

Keywords: Drug Repurposing, Artificial Intelligence, Machine Learning, Pharmacology, Therapeutic Applications, Drug-Disease Associations, Deep Learning, Drug Discovery.