Curiosity-Driven Drug Repurposing Via Mechanism Informed Multimodal AI Framework

Abstract

Using already approved drugs to find new treatments for diseases is a faster and more cost-effective way, especially for rare and neglected conditions. But this method does face few challenges like limited data, complex biological processes, and lack of transparency in some AI models [2]. This research presents an AI system that combines deep learning, meta-learning, and explainable AI to address these issues [8]. The system includes tools for predicting drug combinations, evaluating toxicity and side effects, and extracting some knowledge out from scientific literature [9]. These tools enable the system to apply insights from well-researched diseases to those that are less studied. The system also gives some kind of explanations for its decisions highlighting key genes, pathways, and molecular interactions, and suggesting new potential drug-target relationships that was previously unknown [9]. By integrating prediction, safety assessment, and explanation features, this flexible and scalable system significantly enhances the process discovering new kind of uses for existing drugs [2].