Exploring Deep Learning Strategies in the Drug Discovery and Development Landscape

Abstract

Deep learning has emerged as a transformative force in drug discovery and development, offering innovative strategies to enhance various stages of the pharmaceutical pipeline. This paper explores the application of deep learning techniques in drug discovery, including target identification, virtual screening, drug design, and predicting drug interactions and side effects. We discuss the advantages of deep learning over traditional methods, highlight specific strategies and models employed, and examine future prospects and challenges in integrating these technologies into the pharmaceutical industry.