Drug classification method and device, terminal equipment and storage medium

Abstract

The embodiment of the invention discloses a drug classification method and device, terminal equipment and a storage medium. The method is suitable for digital medical treatment, and comprises the following steps: obtaining an attribute feature vector of each atom in a to-be-detected drug molecule and an attribute feature vector of a virtual atom; and constructing an adjacent matrix according to the connection relationship between the virtual atom and each atom in the n atoms and the connection relationship between the atoms in the n atoms, and constructing an atom attribute feature matrix according to the attribute feature vectors of the n atoms and the virtual atom; inputting the adjacency matrix and the atom attribute feature matrix into a graph neural network to determine a transfer feature matrix of n atoms and virtual atoms through the graph neural network; and determining a molecular feature vector corresponding to the to-be-detected drug molecule according to the transfer feature matrix, and inputting the molecular feature vector into a classifier to output the drug category of the to-be-detected drug molecule through the classifier. By adopting the embodiment of the invention, the accuracy of drug classification can be improved.

Description

technical field [0001] The present application relates to the field of data processing, and in particular to a medicine classification method, device, terminal equipment and storage medium. Background technique [0002] The drug development process has the characteristics of high capital density, high risk, and long cycle, which requires a lot of investment in capital, manpower and material resources. Although traditional machine learning methods can assist drug development to a certain extent, they require molecular descriptors as feature inputs, and the selection of different molecular descriptors has a greater impact on the performance of machine learning models. Therefore, most traditional machine learning methods require Perform tedious and time-consuming feature engineering. In recent years, emerging deep learning methods can directly extract features from the original structure of drugs, thereby bypassing feature engineering and shortening the development cycle. How...

AI Technical Summary

Problems solved by technology

However, when the features of drug molecules extracted based on existing deep learning methods are used to predict drug classification, the prediction accuracy still needs to be improved.

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