Deep Learning in Drug Discovery: Current Landscape and Future Prospects

Abstract

Deep learning (DL) has emerged as a transformative technology in drug discovery, offering the potential to accelerate and optimize various stages of the drug development pipeline. While numerous reviews have summarized the broader landscape of machine learning (ML) in this field, this review focuses specifically on deep learning, highlighting its unique strengths and challenges. We examine the current state-of-the-art deep learning algorithms applied in drug discovery, categorizing them by their architectural designs and applications. We further identify emerging trends and potential areas for future research, emphasizing the need for continued exploration and innovation at the intersection of deep learning and drug discovery