Health

GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction

14 days ago   |   By Springer

Abstract
Background
Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-related data to achieve successful solutions. Modeling the properties and functions of proteins is important but challenging, especially when dealing with predictions of the sequence type.
Result
We propose a method to model compounds and proteins for compound-protein interaction prediction. A graph neural network is used to represent...
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