In silico design of a novel hybrid compound derived from limonoids of Azadirachta indica as an inhibitor of the main protease of SARS-CoV-2
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over 776 million cases and >7 million deaths worldwide since the beginning of the pandemic until September 2024. The main protease (M pro ) of this virus is regarded as a promising target for the development of new therapies. In this sense, the study aimed to explore the biocompounds derived from Azadirachta indica , particularly limonoids, as possible inhibitors of M pro . First, bioactive compounds were selected for their antiviral activity based on a literature review. In pharmacophore modeling, a hybrid model (Limotest) was developed using three limonoids. Pharmacokinetic and toxicological parameters were predicted using the ADMETLab 3.0 and ProTox 3.0 platforms. According to the in silico data, Limotest possesses favorable characteristics for drug development. Molecular docking calculations suggested that Limotest binds spontaneously to both the catalytic and allosteric sites of M pro , exhibiting competitive binding energy. The binding of the compounds involves hydrogen bonds, salt bridges, and hydrophobic interactions, with evidence pointing to their potential for competitive inhibition. The results suggested that Limotest is a potential inhibitor of SARS-CoV-2 M pro and should be considered for future synthesis and cell-based validations.
Authors
- Otávio Augusto Chaves (ORCID: https://orcid.org/0000-0001-6211-7659)
- Márcia Barbosa da Silva (ORCID: https://orcid.org/0000-0002-7450-2332)
- Cassiane da Silva Uzeda (ORCID: https://orcid.org/0000-0002-2984-6507)
- Eduardo Alves da Silva Cazumbá (ORCID: https://orcid.org/0009-0008-6237-3415)
- CAAD Souza (ORCID: https://orcid.org/0009-0001-9856-4773)
- ACC Santana (ORCID: https://orcid.org/0009-0006-3132-048X)
Institutions
- Universidade Federal da Bahia (BR)
- Universidade Estadual Paulista (Unesp) (BR)
Publication Details
- Journal
- South African Journal of Botany
- Published
- 2026-10-14
- DOI
- https://doi.org/10.1016/j.sajb.2026.09.020
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior