Method for drug classification, terminal device, and non-transitory computer-readable storage medium

A terminal device, comprising:

• a processor;
• and
• a memory coupled with the processor and configured to store computer programs, wherein the computer programs comprise program instructions, and the processor is configured to invoke the program instructions to:
• obtain an attribute feature vector of each of n atoms in a drug molecule to be detected and an attribute feature vector of a virtual atom, wherein the virtual atom is connected with each of the n atoms;
• construct an adjacency matrix according to a connection relationship between the virtual atom and each of the n atoms and a connection relationship between the n atoms, and construct an atom attribute feature matrix according to the attribute feature vector of each of the n atoms and the attribute feature vector of the virtual atom;
• input the adjacency matrix and the atom attribute feature matrix into a graph neural network to determine a transfer feature matrix of the n atoms and the virtual atom through the graph neural network;
• and
• determine, according to the transfer feature matrix, a molecular feature vector corresponding to the drug molecule to be detected, and input the molecular feature vector into a classifier to output a drug category of the drug molecule to be detected through the classifier.

A non-transitory computer-readable storage medium storing computer programs, wherein the computer programs comprise program instructions which, when executed by a processor, cause the processor to:

• obtain an attribute feature vector of each of n atoms in a drug molecule to be detected and an attribute feature vector of a virtual atom, wherein the virtual atom is connected with each of the n atoms;
• construct an adjacency matrix according to a connection relationship between the virtual atom and each of the n atoms and a connection relationship between the n atoms, and construct an atom attribute feature matrix according to the attribute feature vector of each of the n atoms and the attribute feature vector of the virtual atom;
• input the adjacency matrix and the atom attribute feature matrix into a graph neural network to determine a transfer feature matrix of the n atoms and the virtual atom through the graph neural network;
• and
• determine, according to the transfer feature matrix, a molecular feature vector corresponding to the drug molecule to be detected, and input the molecular feature vector into a classifier to output a drug category of the drug molecule to be detected through the classifier.