In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning

• Published in: Molecules (Basel, Switzerland) Switzerland), 2022-12, Vol.28 (1), p.208
• Main Authors: Liang, Jihao, Zheng, Yang, Tong, Xin, Yang, Naixue, Dai, Shaoxing
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Antiviral agents; Antiviral Agents - pharmacology; Biological activity; Care and treatment; Chemical fingerprinting; Cheminformatics; Classification; Coronaviruses; COVID-19; COVID-19 Drug Treatment; Datasets; Disease transmission; Drug approval; Drug resistance; Drugs; FDA approval; Health aspects; Herbal medicine; Humans; Informatics; Learning algorithms; Machine Learning; Medicinal plants; Medicine, Botanic; Medicine, Chinese; Medicine, Herbal; Natural products; Pandemics; Plants, Medicinal; Proteins; RNA polymerase; SARS-CoV-2; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Similarity; Support vector machines; Traditional Chinese medicine; Vaccines
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules28010208

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.