Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction.

ABSTRACT: Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by ca

SUBMITTER: Wang X 

PROVIDER: S-EPMC7147459 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature