The invention discloses a
drug molecule recommendation
system for regulating and controlling
disease targets based on a
deep learning method, and belongs to the technical field of
drug relocation, convolutional neural networks and residual networks. The
system comprises a deep residual error
network model, and the deep residual error
network model comprises an embedded network, a
convolution residual error neural network and a full connection layer residual error network. The embedded network converts a
drug molecule SMILES sequence or a
protein amino acid sequence into a binary matrix. The
convolution residual neural network comprises three convolutional
layers, an addition layer and a maximum
pooling layer, and is a network represented by a 'learning'
drug molecule SMILES sequence or
protein amino acid sequence feature, the input of the network is a binary matrix representing a
drug molecule or
protein, and the output of the network is a feature representation vector of the
drug molecule or protein. The full connection layer residual network comprises three full-connection
layers, two dropout
layers and an addition layer, the input is a splicing vector expressed by drug moleculeand protein characteristics and an actual binding affinity value of the drug molecule and the protein characteristics, and the output is a binding affinity prediction value of the drug molecule and the protein.