Abstract
One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.
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