中国药业2024，Vol.33Issue(14)：47-53,7.DOI:10.3969/j.issn.1006-4931.2024.14.012
基于集成深度学习框架的新型冠状病毒感染治疗药物活性预测
Prediction of Drug Activity for COVID-19 Based on Ensemble Deep Learning Framework
摘要
Abstract
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.关键词
集成深度学习框架/新型冠状病毒感染/药物活性/神经网络/自动生物序列Key words
ensemble deep learning framework/Corona Virus Disease 2019/drug activity/neural network/autoBioSeqpy分类
医药卫生引用本文复制引用
许强,罗杰斯,杨明,张永林..基于集成深度学习框架的新型冠状病毒感染治疗药物活性预测[J].中国药业,2024,33(14):47-53,7.基金项目
西南特色中药资源国家重点实验室开放基金[SKLTCM2022028] （）
川北医学院校级科研发展计划项目[CBY22-QNA38]. （）
