Graph Neural Network (GNN) and neural network, an end-to-end graph embedding technique based on Graph Signal Processing (GSP), which aggregates the topological information of the neighborhoods of each node in a
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted fr...
14,15. Others, taking a more ambitious approach, aiming to generate binding molecular conformations within the pocket. Models like LiGAN16, GraphBP17, and DiffSBDD18aim to produce pocket-aware ligands with topology and three-dimensional (3D) geometry directly within the pocket. However, in tackli...
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted fr...
The process begins with an initial graph-encoded material serving as the input. Following this, multiple Augmented Graph Attention (AGAT) layers, each containing 64 neurons, and a DGN (Dynamic Graph Network) are utilized. There is a skip connection from the output of the l-th AGAT layer to...
Previously, many works often utilized graphs as input to enable the graph networks to learn the 2D topological information of molecules [2], [3]. For example, AttentiveFP [4] used a graph attention network to predict molecular properties by aggregating and updating node information. The MP-GNN...
Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the siamese network architecture ...
Specifically, we first design a novel tensor-product-based high-order graph attention network (GAT) with structural constraints to realize efficient attribute fusion and semantic consistency encoding. By imposing attribute augmentation mechanisms and smooth constraints (SCs) on the proposed high-order ...
Le, T., Noe, F., Clevert, D.A.: Representation learning on biomolecular structures using equivariant graph attention. In: The First Learning on Graphs Conference (2022). https://openreview.net/forum?id=kv4xUo5Pu6 Luo, S., Guan, J., Ma, J., Peng, J.: A 3d generative model for ...
15 improved this method by using an E(3)-equivariant graph neural network (GNN), which respects rotation and translation symmetries in 3D space. Similarly, Drotár et al.16 and Liu et al.17 used autoregressive models to generate atoms sequentially and incorporate angles during the generation ...