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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Otávio Augusto Chaves, Márcia Barbosa da Silva, Cassiane da Silva Uzeda, Eduardo Alves da Silva Cazumbá et al.
South African Journal of Botany
Computational Drug Discovery Methods
article

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

Otávio Augusto Chaves, Márcia Barbosa da Silva, Cassiane da Silva Uzeda, Eduardo Alves da Silva Cazumbá, CAAD Souza, ACC Santana
article en

Abstract

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.

South African Journal of BotanyVol. 198
Universidade Federal da Bahia (BR), Universidade Estadual Paulista (Unesp) (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.