Compound-protein interaction prediction by deep learning: Databases, descriptors and models

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.

Automated summary

This review comprehensively investigates DL-based CPI prediction, covering popular databases, representation methods, and state-of-the-art models. Current challenges and trends suggest that crucial progress lies in better CPI prediction and key approaches in practical applications.