To address above challenges, we propose a novel Curvature-based Adaptive Graph Neural Network (CurvAGN) for predicting protein-ligand binding affinity. The CurvAGN comprises a curvature block and an adaptive attention guided neural block (AGN). The curvature block assigns edge attributes to include ...
predict the in-different size.Take Fig.2 for example again,when these twoeak acids are neutralized with the same strong base, the in- teraction label R∈L of an unseen entity pair (GX,GY)new.teraction can be accurately modeled by features of the secondonvolution layer forAcetic acid ...
As we notice above, the regression tasks for some of the electronic properties do not show very high MAD: MAE. we train classification models for some of them. Classification tasks predict labels such as high value/low value (based on a selected threshold) as 1 and 0 instead of predicting ...
for computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction framework based on graph convolutional neural networks (GCNs). In contrast to current CNN methods, GraphProt2 offers native support for the encoding of base pair ...
Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macr
Stock markets are dynamic systems that exhibit complex intra-share and inter-share temporal dependencies. Spatial-temporal graph neural networks (ST-GNN) are emerging DNN architectures that have yielded high performance for flow prediction in dynamic sys
EASSY 1 Temporal Relational Ranking for Stock Prediction This eassy contributed a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. The key novelty of this work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, ...
To effectively predict the PM2.5 concentrations, we propose a graph attention recurrent neural network (GARNN) model by taking into account both meteorological and geographical information. Extensive experiments validated the efficiency of the proposed GARNN model, revealing its superior...
Inspired by the GN framework, we first construct a topological graph of the road network and feed the whole graph into the neural network and obtain a graph as output. Moreover, we combine the LSTM and GN block to build a new model to predict the traffic speed, where an encoder-decoder...
In drug design, compound potency prediction is a popular machine learning application. Graph neural networks (GNNs) predict ligand affinity from graph representations of protein–ligand interactions typically extracted from X-ray structures. Despite some