Drug-protein interaction prediction model based on convolutional neural network

Abstract

The invention provides a drug-protein interaction prediction model based on a convolutional neural network, and the construction method of the prediction model comprises the following steps: 1, constructing a bounding box descriptor for a binding site of a target protein, and extracting the spatial structure characteristics of the multi-channel binding site by using a three-layer 3D convolutional neural network; 2, based on the amino acid sequence of the target protein, extracting amino acid composition characteristics of the protein by using a three-layer 1D convolutional neural network; 3, constructing a molecular map for to-be-screened drug molecules, and extracting drug molecule features by using a three-layer map convolutional neural network; and 4, combining all the obtained features to obtain an overall feature, and inputting the overall feature into the two-layer full-connection network to predict drug-protein interaction, so that the method not only considers local features of binding sites closely related to the docking process, but also considers global features of protein, and the characteristics are used for predicting the compound-protein interaction.

Description

technical field

[0001] The invention belongs to the technical field of drug-protein interaction prediction, in particular to a drug-protein interaction prediction model based on convolutional neural network. Background technique

[0002] Drug discovery involves multiple steps, takes a long time and costs a lot of money. Prediction and identification of compound-protein interactions (CPIs) play a crucial role in the discovery and development of safe and effective new drugs. In the early stage of drug discovery, screening out compounds that interact with target proteins can greatly improve the success rate of drug discovery. A large number of studies have shown that the advantage of deep learning is that it can obtain robust descriptors of the original data after nonlinear transformation, which can facilitate the model to learn task-related features from the data. With the establishment of more and more protein structure and compound-protein interaction datasets, more and mo...

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