Method for enhancing point-edge interaction of graph neural network
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A neural network and node technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as ignoring transferability
Active Publication Date: 2020-10-30
SUN YAT SEN UNIV
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[0006]
The present invention aims at overcoming at least one defect of the above-mentioned prior art, and provides a method for enhancing the point-edge interaction of the graph neural network, which is used to solve the problem of ignoring the transfer between the edge and the point in the directed graph in the existing prediction method. defect
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Embodiment 1
[0042] This embodiment provides a method for enhancing the point-edge interaction of the graph neural network, such as figure 1 As shown, the graph neural network includes an input layer, a feature extraction layer, an information interaction layer, and an output layer, wherein the method provided in this embodiment is applied to the entire neural network, and the specific steps are as follows:
[0043] S1: The input layer acquires the directed graph G(V, E) of the application object, and the feature extraction layer extracts the graph structure data of the graph G, wherein the V is all nodes of the directed graph G , among all nodes, any node is denoted as v, any adjacent node of any node v is denoted as w; said E is all edges of the directed graph G, among all edges, any edge is denoted as e, so The node v is any element in the application object, and the edge e is the association relationship between any connected two elements in the application object; the application obje...
Embodiment 2
[0089] This embodiment provides a method for enhancing the point-edge interaction of the graph neural network, which is used to predict the core features of the directed graph of the molecular structure. The graph neural network includes an input layer, a feature extraction layer, an information interaction layer, and an output layer, wherein , the method provided in this embodiment is applied to the entire neural network, and the specific steps are as follows:
[0090] S1: The input layer obtains the directed molecular graph G(V, E), and the feature extraction layer extracts the graph structure data of the molecular graph G, wherein the V is all atoms of the directed molecular graph G , among all atoms, any atom is represented as v, and any adjacent node of any atom v is represented as w; the E is all bonds of the directed molecular graph G, among all bonds, any bond is represented as e, The bond represents the association relationship between the connected atoms;
[0091] A...
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Abstract
The invention provides a method for enhancing point-edge interaction of a graph neural network.; obtaining a directed molecular graph G and graph structure data thereof; according to the graph structure data, obtaining all node original features Xv in all and the graph structure data according to all and all created node original features Xv in all and the graph structure data according to all andall created node original features Xv in all and the graph structure data; the graph neural network is iterated to the Kth layer, a final node representation form h (v) of the directed molecular graph is obtained, k is larger than or equal to 1, and K is larger than k; the hidden representation from the adjacent node w of each arbitrary node v to the edge of the arbitrary node v is utilized, namely, the message vector of the arbitrary node v on the kth layer is created, so that the information of the edge is associated and transmitted with the node information, the embedding of the nodes andthe edge is updated in the neural network training process, and the transmissibility of the information between the nodes and the edge is concerned.
Description
technical field [0001] The invention relates to the field of novel graph neural networks, and more specifically, to a method for enhancing point-edge interaction of graph neural networks. Background technique [0002] Accurate prediction of molecular properties has always been a topic of continuous concern in the pharmaceutical industry. The main goal of molecular property prediction is to remove compounds that are more prone to property burdens in the downstream development process, thereby saving a lot of resources and time. [0003] Related research methods to predict the properties of molecules have gone through two stages: traditional methods and machine learning methods. Traditional approaches, primarily quantitative structure-property relationships (QSPR) based on feature engineering, limit the ability of models to learn beyond the fringes of existing chemical knowledge. Machine learning and especially deep learning methods have shown great potential to compete with ...
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