Enhancing Drug Discovery and Repurposing through Transformer Models and Reinforcement Learning Algorithms

Abstract

This research paper investigates the integration of transformer models and reinforcement learning algorithms in advancing drug discovery and repurposing processes. The study leverages the superior natural language processing capabilities of transformer architectures, specifically BERT and GPT variants, to efficiently analyze extensive pharmaceutical data, including chemical structures, genomic sequences, and biomedical literature. By employing transfer learning techniques, these models are adept at identifying potential drug candidates and predicting their interactions with biological targets. Concurrently, reinforcement learning algorithms are utilized to optimize the drug repurposing pipeline, facilitating the identification of existing compounds with possible new therapeutic applications. The approach is validated through a series of experiments focusing on identifying repurposable drugs for neglected diseases, achieving a significant increase in prediction accuracy and discovery speed compared to traditional methods. The results demonstrate that the combination of transformer models and reinforcement learning presents a compelling strategy for reducing the time and costs associated with drug development, while also expanding the potential drug repertoire. This synergy offers promising implications for accelerating biomedical innovations and personalized medicine solutions. The paper concludes with a discussion on potential challenges, such as data scarcity and model interpretability, and proposes future directions for integrating advanced computational techniques in pharmaceutical research.