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

Bing-Xue Du, Yuan Qin, Yan-Feng Jiang, Yi Xu, Siu-Ming Yiu, Hui Yu, Jian-Yu Shi

Drug discovery today 2022 May

filter terms:

databases (2)

databases factual (1)

investigates (1)

review state- art (1)

Sizes of these terms reflect their relevance to your search.

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. Copyright © 2022 Elsevier Ltd. All rights reserved.

Citation

Bing-Xue Du, Yuan Qin, Yan-Feng Jiang, Yi Xu, Siu-Ming Yiu, Hui Yu, Jian-Yu Shi. Compound-protein interaction prediction by deep learning: Databases, descriptors and models. Drug discovery today. 2022 May;27(5):1350-1366