부산대학교 도서관

• Massive compound–protein interaction entries are available for DL. • Diverse compound fingerprints and varied protein descriptors are fundamental for DL. • LSTM, CNN and GNN boost advanced representations of compounds and proteins. • Various DL-based predictive models commonly use Y-shaped architecture. • Desired models tend toward weakly supervised representations and high interpretability. The screening of compound–protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with the advantage of low cost and high efficiency. This review provides a comprehensive survey of DL-based CPI prediction. It first summarizes popular databases of small-molecule compounds, proteins and binding complexes. Then, it outlines classical representations of compounds and proteins in turn. After that, this review briefly introduces state-of-the-art DL-based models in terms of design paradigms and investigates their prediction performance. Finally, it indicates current challenges and trends toward better CPI prediction and sketches out crucial approaches toward practical applications. [ABSTRACT FROM AUTHOR]