Prediction of Drug Activity for COVID-19 Based on Ensemble Deep Learning Framework
Objective To establish an ensemble deep learning framework for predicting the activity of drugs for Corona Virus Disease 2019(COVID-19).Methods Convolutional neural network(CNN)and recursive neural network(RNN)were used to screen the representative feature identifiers from the simplified molecular input line entry system(SMILES)sequence.Deep neural network(DNN)was used to extract higher-level abstract features from discrete feature information.The optimal structure of one main framework model and seven discrete feature models was generated by the grid search method,forming 127 possible combinations of eight architectures.The predictive performance of model was evaluated by the accuracy(ACC),F,Recall,precision(PRE)and Matthews correlation coefficient(MCC).The final framework was established and maintained.Results An ensemble deep learning model with BiLSTM as the core architecture and consisting of four different discrete feature models was ultimately established.The ACC of the training set was 72.84%,the F was 69.70,the Recall was 72.21%,the PRE was 68.03,and the MCC was 0.456 9.Twenty-three drugs that might be effective against COVID-19 were successfully predicted in the test set.Conclusion The ensemble deep learning framework has better predictive performance than a singular model,this study provides a new choice for the screening of the drugs for COVID-19.
ensemble deep learning frameworkCorona Virus Disease 2019drug activityneural networkautoBioSeqpy
国家科技期刊平台
NSTL
万方数据
