Screening Herbal Compound Candidates for Use as Anti-Inflammatory Drugs for COVID-19 Treatment Using Deep Semisupervised Learning

Abstract

COVID-19 is a disease caused by the SARS-CoV-2 virus. Its symptoms include cough, fever, shortness of breath, and acute inflammation (hyperinflammation), and severe cases can lead to death. These symptoms tend to worsen if inflammation is not controlled. This research aims to build a stacked autoencoder deep neural network (SAE-DNN) model for identifying herbal compound candidates that can be used as anti-inflammatory drugs for COVID-19 treatment. The model’s performance is evaluated on the basis of different data representations. The research process involves data collection, data preprocessing, modeling, and testing the model on the herbal data to obtain herbal compound candidates. Results indicate that the developed SAE-DNN model with compound representation that combines fingerprints and dipeptide composition produces the best performance with an accuracy of 0.96722, a recall of 0.96419, area under the receiver operating characteristic of 0.99596, and an F1 score of 0.96567. A total of 33 herbal compounds are found as candidate anti-inflammatory drugs by using the SAE-DNN model.