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

Abstract

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.

In-silico approaches for identification of compounds inhibiting SARS-CoV-2 3CL protease

“…It is essential to determine the molecular interaction of the ligands with the target protein to estimate the therapeutic and inhibitory potential of a given compound. There has been a recent addition of a new dimension through the use of machine learning techniques with virtual drug screening methods for the creation of novel medications [33,35,[40][41][42][43][44][45][46], disabling multidrug resistance [47], and applications in precision medicine to choose drugs for customised treatments [48,49]. Several studies were reported which demonstrate the application of machine learning for predicting potential inhibitory compounds for SARS-CoV-2.…”

“…Similarly, another study used a deep learning model to predict the inhibitory activity against 3CLpro in SARS-CoV for unknown compounds in the virtual screening process, as reported by Kumari et al [51]. Random forest (RF) and support vector machine (SVM) models were used in a study by Liang et al to hunt novel anti-SARS-CoV-2 compounds from medicinal plants using traditional Chinese medicine (TCM) principle applying machine learning methods [44]. One study also developed a machine learning suite called "REDIAL-2020" to estimate small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays [52].…”