Deep Learning in Drug Discovery: Current Landscape and Future Prospects
Sensen Liu and Yin-Shan Lin
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.
