Ligand screening model construction method and device, a screening method, a device, and a medium

A screening drug ligands method, comprising:

• drawing a target ligand graph network based on a ligand chemical formula of the target ligand, in which atoms are nodes and chemical bonds are edges connecting the nodes;
• inputting the target ligand graph network into a ligand screening model;
• and
• outputting an analysis result of the target ligand, wherein the ligand screening model is trained by the method comprising:
• a ligand screening model construction method, which comprises:
• obtaining a drug ligand training set, wherein the drug ligand training set includes a drug ligand chemical formula and one or more classification labels;
• based on the ligand chemical formula, drawing the ligand graph network, in which atoms are nodes and chemical bonds are edges connecting the nodes;
• using a random initialization vector to identify a weight vector of each node in the ligand graph network;
• reconstructing each node of the ligand graph network according to a connection relationship of the ligand graph network to obtain a reconstruction network, and repeating the reconstruction step to obtain at least two layers of reconstruction networks;
• performing deep learning on the ligand graph network and the at least two-layer reconstruction graph network according to one or more classification labels;
• and
• constructing the ligand screening model.

A ligand screening model construction device, characterized in that the device comprises:

• a training set acquisition module, configured to acquire a drug ligand training set, wherein the drug ligand training set includes a drug ligand chemical formula and a classification label;
• a network drawing module is used to draw a ligand graph network based on the ligand chemical formula, in which atoms are nodes and chemical bonds are edges connecting nodes;
• a vector identification module configured to identify a weight vector of each node in the ligand graph network by using a random initialization vector;
• a reconstruction module used to reconstruct each node of the ligand graph network according to a connection relationship of the ligand graph network to obtain a reconstruction graph network, and repeating the reconstruction steps to obtain at least two layers of reconstruction networks;
• and
• a model training module used for deeply learning the ligand graph network and at least two layers of reconstruction graph networks according to the classification labels and construct a ligand screening model.

A computer device comprising a memory and a processor, wherein the memory stores a computer program, and is characterized in that when executing the computer program, the processor implements steps of a method comprising:

• a ligand screening model construction method, which comprises:
• obtaining a drug ligand training set, wherein the drug ligand training set includes a drug ligand chemical formula and one or more classification labels;
• based on the ligand chemical formula, drawing a ligand graph network, in which atoms are nodes and chemical bonds are edges connecting the nodes;
• using a random initialization vector to identify a weight vector of each node in the ligand graph network;
• reconstructing each node of the ligand graph network according to a connection relationship of the ligand graph network to obtain a reconstruction network, and repeating the reconstruction step to obtain at least two layers of reconstruction networks;
• performing deep learning on the ligand graph network and the at least two-layer reconstruction graph network according to one or more classification labels;
• and
• constructing the ligand screening model.

A computer readable storage medium on which a computer program is stored, is characterized in that when a processor execute the computer program, implementing the steps of a method comprising:

• a ligand screening model construction method, which comprises:
• obtaining a drug ligand training set, wherein the drug ligand training set includes a drug ligand chemical formula and one or more classification labels;
• based on the ligand chemical formula, drawing a ligand graph network, in which atoms are nodes and chemical bonds are edges connecting the nodes;
• using a random initialization vector to identify a weight vector of each node in the ligand graph network;
• reconstructing each node of the ligand graph network according to a connection relationship of the ligand graph network to obtain a reconstruction network, and repeating the reconstruction step to obtain at least two layers of reconstruction networks;
• performing deep learning on the ligand graph network and the at least two-layer reconstruction graph network according to one or more classification labels;
• and
• constructing the ligand screening model.