Virtual drug repurposing of Z-Guggulsterone for SARS-CoV-2 using machine learning and molecular simulations
Year: 2026
Type: article
Abstract: COVID-19, a disease caused by SARS-CoV-2, is classified as a pandemic that claims millions of lives worldwide. Progress has been made in advancing therapies and vaccines against this virus. However, it is imperative to identify novel therapeutics due to the nature of the virus and the limitations of drugs and vaccines that have been developed. There has been an increasing interest in investigating natural products as a potential treatment for COVID-19. Guggulsterone, a bioactive molecule of the Commiphora wightii, is widely reported for its medicinal applications. In this study, the docking and molecular dynamics (MD) simulations of Guggulsterone with SARS-CoV-2 proteins (spike glycoprotein, papain-like protease, receptor binding domain, main protease, and RTC (Replication and Transcription Complex) were carried out to evaluate the antiviral potential of Guggulsterone against SARS-CoV-2. This study revealed a strong affinity of Z -Guggulsterone with all the selected SARS-CoV-2 target proteins with binding energy ranging from −8.4 to −5.7 kcal/mol. Z-Guggulsterone was identified to form the most stable complex with papain-like protease; it exhibited strong hydrogen bonding with key catalytic residues of papain-like protease. The predicted pIC50 value of 4.81, using a developed neural network model, indicates a moderate potential for bioactivity. Z -Guggulsterone also has significant interactions and moderate stability with the main protease, spike glycoprotein, and RTC proteins, while the receptor binding domain complex showed large structural deviations. The affinity and stability of papain-like protease with Z -Guggulsterone, as well as the predicted pIC50 value, indicate a promising antiviral potential. The findings reported in this instigations are solely in silico and these computational results necessitate future experimental validation before therapeutic relevance can be established.
Source: Results in Chemistry
Authors: Jaykumar Koradiya, Ashok Kumar Bishoyi
Institution: Marwadi University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,870
OpenAlex ID: W7119119048