Molecular property prediction method and device based on heterogeneous graph neural network, and medium

Abstract

The application relates to the field of drug discovery and molecular design, and particularly relates to a molecular attribute prediction method and device based on a heterogeneous graph neural network and a medium. The method obtains structure information of a to-be-predicted molecule and a target attribute category to be predicted; extracts atomic-level features to construct atomic nodes and atom-atom edges; connects corresponding atomic nodes and pharmacophore nodes; connects virtual molecule nodes and attribute nodes, so as to construct a task-specific heterogeneous molecular graph containing atomic nodes, globally shared pharmacophore nodes, attribute nodes, virtual molecule nodes, atom-atom edges, atom-pharmacophore edges, molecule-attribute edges and pharmacophore-attribute edges; inputs the graph into a pre-trained heterogeneous graph neural network model; generates a global representation vector through a cross-layer adaptive attention mechanism, and outputs a prediction result of the target attribute category. The method can capture the complex hierarchical relationship in a molecule and realize effective molecular attribute prediction under a small sample condition.

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