Molecular graph comparison learning method based on chemical element knowledge graph

Abstract

The invention discloses a molecular graph comparison learning method based on a chemical element knowledge graph. The method comprises the following steps of: constructing a chemical element knowledge graph according to all chemical attributes of each chemical element in a periodic table of chemical elements; performing graph enhancement on the molecular graph by utilizing the chemical element knowledge graph to obtain a molecular enhancement graph; obtaining graph representations of the molecular graph and the molecular enhancement graph by using a pluggable representation model; adopting a hard negative sample mining technology to select other molecular graphs similar to the molecular graph in the molecular fingerprint space as negative samples; mapping the graph representation of positive sample pairs and the graph representation of negative sample pairs to the same space, constructing a contrast loss function by maximizing the consistency between the positive sample pairs and minimizing the consistency between the negative sample pairs, and performing optimization learning by using the contrast loss function; and forming a prediction model by using the parameter-determined pluggable representation model and a nonlinear classifier, and predicting the molecular properties of the molecular graph by using a parameter-finely-adjusted prediction model so as to improve the prediction accuracy of molecular properties.

Description

technical field [0001] The invention relates to the field of diagram comparison learning, in particular to a molecular diagram comparison learning method based on a chemical element knowledge map. Background technique [0002] Over the past few years, Graph Representation Learning (GRL) has become a popular research area for analyzing graph-structured data. Graph representation learning aims to learn an encoding function that transforms graph data with complex structures into a dense representation in a low-dimensional space that preserves diverse graph properties and structural features. [0003] Traditional graph representation learning methods use random walks to convert graphs into node sequences to model the co-occurrence relationship between central nodes and neighbor nodes. However, this type of learning framework has two obvious shortcomings: one is the lack of parameter sharing between encoders, which will take up too much computing resources; the other is that the...

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Problems solved by technology

However, this type of learning framework has two obvious shortcomings: one is the lack of parameter sharing between encoders, which will take up too much computing resources; the other is that the model lacks generalization ability and is difficult to apply to new graphs.

But this contrastive framework has two obvious disadvantages when applied to molecular graphs: first, removing or adding chemical bonds or groups will change the properties and chemical meaning of molecular graphs; second, atoms in molecular graphs are modeled as Individuals that can only be connected by chemical bonds do not consider the correlation at the microscopic level of atoms (the commonality between atoms with the same attributes)

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