Although extensive efforts have been made to improve QA accuracy, it is still the bottleneck of current protein docking systems. In this paper, we presented a Deep Graph Attention Neural Network ...

Nowadays, there are two significant trends in deep learning for molecular docking: (i) the augmentation of available structural data and (ii) the use of a new kind of neural network: the graph ...

2021). Interestingly, these graph-based neural networks are gaining new adaptations and, because of this, constantly exhibit better performance than conventional molecular docking programs, such as ...

Afterward, molecular docking simulations were used to investigate the binding positions of aptamers and receptors. As the graph neural network (GNN) proposed, our findings show that the aptamers bind ...

GNN-PL-Docking is a repository for protein–ligand docking using Graph Neural Networks (GNNs). The repository contains re-implementations inspired by recent state-of-the-art approaches—including DI-GNN ...

graph convolution-based fingerprints as a base into an artificial neural network. The architecture is efficient, explainable, and performant as a tool for the binary classification of ligands based on ...

Got it now: “Graph Neural Networks ... within the graph. These networks have been successfully used in applications such as chemistry and program analysis. In this introductory talk, I will do a deep ...