Protein drug binding site prediction method based on deep learning

Abstract

The invention provides a protein drug binding site prediction method based on deep learning, and the method comprises the following steps: 1, selecting a plurality of proteins in a protein database toform a training set, a plurality of proteins to form a verification set, and a plurality of proteins to form a test set, wherein the training set being used for training a training model; step 2, carrying out feature extraction and label extraction on the protein database through the trained training model to obtain data, finishing training of a neural network and obtaining a prediction model; and 3, inputting the new protein into the prediction model, and positioning and predicting the position of the binding site. The method has the advantages that the forming factors of the binding site are comprehensively considered, and the binding site is positioned and predicted based on deep learning.

Description

technical field

[0001] The invention belongs to the field of binding site prediction methods, in particular to a method for predicting protein drug binding sites based on deep learning. Background technique

[0002] At present, with the emergence of new diseases and the increase of drug resistance of pathogens such as viruses and bacteria, the research and development of drugs is facing enormous pressure. The research and development of drugs needs to go through multiple processes such as the discovery of lead compounds, clinical trials, marketing and tracking, which consumes a lot of manpower, material resources, financial resources and time. The emergence of computer-aided drug design has solved some problems to a certain extent. Computer-aided drug design provides assistance for drug development by using computers to simulate the metabolism and screening of drugs. In computer-aided drug design, virtual screening is a key step, which lays the foundation for the discovery ...

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