Drug and target interaction prediction method and device, equipment and storage medium

Abstract

The invention relates to the technical field of neural networks of the artificial intelligence technology, and provides a drug and target interaction prediction method, device and equipment and a storage medium, and the method comprises the steps: calling a pre-constructed graph neural network to extract drug features in a molecular graph of a drug, processing the molecular map according to a restart random walk algorithm, predicting the similarity of drug characteristics between two adjacent nodes in the molecular map to obtain global structure information, inputting the global structure information into a preset deep neural network to obtain low-dimensional characteristic information of drugs, and obtaining a protein sequence of a target, and calling a long-short-term memory network to process the protein sequence to obtain protein features, and inputting the low-dimensional feature information and the protein features into a preset full-connection layer to predict an interaction result of the drug and the target so as to extract information contained in the drug and the protein sequence in a targeted manner. And the prediction efficiency of the interaction between the drug and the target is improved.

Description

technical field [0001] The present invention relates to the technical field of neural network of artificial intelligence technology, and in particular, the present invention relates to a method, device, device and storage medium for predicting the interaction between a drug and a target. Background technique [0002] Drug R&D is a systematic project, facing challenges such as high cost, long cycle, and low success rate. The first step in determining whether a molecule can act as a drug is to analyze the ability of a drug to interact with a specific target to determine which drug functions need to be increased or inhibited, so predicting drug-target interactions is a key issue in drug discovery and retargeting studies. , which helps to analyze the effectiveness of drug molecules and is a key step in drug development and reuse. It can also understand the mechanism of drug action, disease pathology, minimize adverse side effects caused by drug molecules, and then assist in the ...

AI Technical Summary

Problems solved by technology

[0003] However, due to the limitations of precision and cost, it is often difficult to obtain large-scale drug-target interaction pairs by traditional biological experimental methods, resulting in very limited drug-target interaction pairs that have been experimentally identified and verified so far. Large-scale verification of biological experimental methods is inefficient and expensive

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