Enabling Transfer Learning for Generalized and Explainable Drug-Repurposing Employing Reinforcement Learning

Abstract

The following thesis presents the research to promote generalizability and explain ability across multiple drug-disease contexts to integrate transfer learning into a re inforcement learning framework. To address and overcome the limitations of tradi tional drug repurposing approaches, task adaptive attention mechanisms and transfer learning framework techniques are integrated. The proposed methodologies increase the efficiency and efficacy of drug repurposing attempts by making the models more adaptable to various conditions while focusing on the enhancing transparency, adapt ability, and model performance. The evaluations that demonstrate interpretabil ity, accuracy and efficacy compared to baseline reinforcement models. The results achieved from this model indicate that it not only expands the application of RL in the field of medical AI, but also makes these AI systems more transparent and trust worthy.