MGCN: Graph Convolutional Network for Drug Molecular Screening

Abstract

The intersection of drug research and development and neural network research is getting closer, new methods and new technologies are emerging one after another, using neural network to improve the efficiency of drug research and development, and rapidly innovate the treatment of diseases. In drug development, one target protein corresponds to multiple drug molecules. In order to save the time and cost of new drug development, virtual screening of key steps in new drug development is studied. A virtual screening model for drug molecules is proposed, and the graph convolutional neural network is used to classify drug molecules to achieve the purpose of preliminary screening. The graph convolutional network model is constructed based on DGL and combined with cross-entropy and fully connected networks. The model is carried out by experimental means analysis. The experimental results show that the graph neural network can complete the classification task of drug molecules. Through the verification of molecular docking software, the screened molecules are real and effective, reducing the time spent on virtual screening, and it has become an inspiration for further exploration of graph neural networks in drug development.