A graph-based multilevel property extraction network for predicting protein drug target binding affinity

Abstract

Background: As the basic material of life, protein regulates physiological activities such as material in and out of cells, signal transduction, metabolism and so on. However, studies have shown that proteins cannot perform these functions alone in cells, and need to be combined with ligands to perform functions. background: As the basic material of life, protein regulates physiological activities such as material in and out of cells, signal transduction, metabolism and so on. However, studies have shown that proteins cannot perform these functions alone in cells, and need to be combined with ligands to perform functions. Objective: The discovery of drugs is based on this mechanism to be developed. But at present, it is difficult to discover new drugs through biological experiments, which lead to high cost, deteriorate the environment and make the human body more resistant to drugs. objective: The discovery of drugs is based on this mechanism to be developed. But at present, it is difficult to discover new drugs through biological experiments, which lead to cost high, deteriorate the environment and make human body more resistant to drugs. The rapid development of computer can assist researchers to screen out potential drugs that can bind to proteins in advance. In the past few decades, most of drug-target affinity prediction methods have disadvantages in high requirements on data set and difficulties to predict the binding strength of drug-target. Method: The rapid development of computers can assist researchers in screening potential drugs that can bind to proteins in advance. In the past few decades, most of the protein drug-target affinity prediction methods have had disadvantages of high requirements on data set and difficulties to predict the binding strength of drug-target. method: This paper proposes a multi-level feature extraction model based on graph convolution to predict drug-target affinity. The model uses an integrated neural network of CNN and GNN to learn the characteristics of the input data of drug-targets. Results: This paper proposes a multilevel feature extraction model based on graph convolution to predict protein drug-target affinity. The model uses an integrated neural network of Text CNN and GNN to learn the characteristics of the input data of drug-targets. result: Experimental results on the benchmark datasets of Davis and Kiba showed that the proposed graph-based convolution network achieves good performance on drug-target affinity prediction. Conclusion: Experimental results on the benchmark datasets of Davis and Kiba showed that the proposed graph-based convolution network achieves good performance on drug-target affinity prediction. conclusion: The results showed that the multi-level feature extraction network model based on graph convolution (GCN-GAT-GCN) is more effective in learning molecular feature information. other: None