In Silico and Molecular Docking Analysis of Benzyl Isothiocyanate from Salvadora persica as a Predicted Multi-Target Candidate for COVID-19 Host Responses
Year: 2026
Type: article
Abstract: COVID-19 remains a relevant area of biomedical investigation because its pathogenesis involves complex virus–host interactions. This study aimed to explore, through purely in silico and hypothesis-generating insights, the predicted molecular associations between benzyl isothiocyanate (BITC) from Salvadora persica and COVID-19-associated host-response pathways. BITC-associated targets were collected from compound-target databases, while COVID-19-associated targets were obtained from disease-gene databases and transcriptomic datasets. Overlapping targets were analyzed using protein–protein interaction network construction, hub-gene prioritization, Gene Ontology and KEGG enrichment analyses, and molecular docking. A total of 271 unique BITC-associated targets and 1890 COVID-19-associated targets were identified, with 36 candidate targets overlapping. PPI analysis generated a connected network of 24 nodes and 39 edges. Hub-gene analysis prioritized ACE, JUN, MAOA, CDK1, MAOB, HCK, CCNA2, ACHE, GADD45A, and ADRA2A. Enrichment analysis indicated associations with inflammatory response, vascular regulation, calcium homeostasis, monoamine oxidase activity, NF-κB signaling, serotonergic synapse, and tryptophan metabolism. Validated active-site docking of six targets yielded comparative Vina scores ranging from −5.380 to −6.437 kcal/mol. These preliminary findings provide theoretical target–pathway associations supporting further investigation of BITC as a potential immunomodulatory candidate within COVID-19-related host-response pathways.
DOI: 10.3390/cimb48080849
Institution: King Saud University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
Funder: King Saud University
Award: G7094201565
OpenAlex ID: W7203907907