GreenMolBD: Nature Derived Bioactive Molecules' Database.
Bangladeshi plant database
Plant database
compound database
in silico properties
natural products
pharmacological evidence
Journal
Medicinal chemistry (Shariqah (United Arab Emirates))
ISSN: 1875-6638
Titre abrégé: Med Chem
Pays: Netherlands
ID NLM: 101240303
Informations de publication
Date de publication:
2022
2022
Historique:
received:
01
09
2021
revised:
26
09
2021
accepted:
04
10
2021
pubmed:
1
12
2021
medline:
13
4
2022
entrez:
30
11
2021
Statut:
ppublish
Résumé
One of the essential resources for developing new drugs are naturally derived biologically active lead compounds. Biomedical researchers and pharmaceutical companies are highly interested in these plant-derived molecules to develop the new drug. In this process, collective information of the plants and their phytoconstituents with different properties and descriptors would greatly benefit the researchers to identify the hit, lead or drug-like compound. Therefore, the work intended to develop a unique and dynamic database Green- MolBD to provide collective information regarding medicinal plants, such as their profile, chemical constituents, and pharmacological evidence. We also aimed to present information of phytoconstituents, such as in silico description, quantum, drugability and biological target information. For data mining, we covered all accessible literature and books, and for in silico analysis, we employed a variety of well-known software and servers. The database is integrated by MySQL, HTML, PHP and JavaScript. GreenMolBD is a freely accessible database and searchable by keywords, plant name, synonym, common name, family name, family synonym, compound name, IUPAC name, InChI Key, target name, and disease name. We have provided a complete profile of individual plants and each compound's physical, quantum, drug likeliness, and toxicity properties (48 type's descriptor) using in silico tools. A total of 1846 associated targets related to 6,864 compounds already explored in different studies are also incorporated and synchronized. This is the first evidence-based database of bioactive molecules from medicinal plants specially grown in Bangladesh, which may help explore and foster nature-inspired rational drug discovery.
Sections du résumé
BACKGROUND
One of the essential resources for developing new drugs are naturally derived biologically active lead compounds. Biomedical researchers and pharmaceutical companies are highly interested in these plant-derived molecules to develop the new drug. In this process, collective information of the plants and their phytoconstituents with different properties and descriptors would greatly benefit the researchers to identify the hit, lead or drug-like compound.
AIM AND OBJECTIVE
Therefore, the work intended to develop a unique and dynamic database Green- MolBD to provide collective information regarding medicinal plants, such as their profile, chemical constituents, and pharmacological evidence. We also aimed to present information of phytoconstituents, such as in silico description, quantum, drugability and biological target information.
METHODS
For data mining, we covered all accessible literature and books, and for in silico analysis, we employed a variety of well-known software and servers. The database is integrated by MySQL, HTML, PHP and JavaScript.
RESULTS
GreenMolBD is a freely accessible database and searchable by keywords, plant name, synonym, common name, family name, family synonym, compound name, IUPAC name, InChI Key, target name, and disease name. We have provided a complete profile of individual plants and each compound's physical, quantum, drug likeliness, and toxicity properties (48 type's descriptor) using in silico tools. A total of 1846 associated targets related to 6,864 compounds already explored in different studies are also incorporated and synchronized.
CONCLUSION
This is the first evidence-based database of bioactive molecules from medicinal plants specially grown in Bangladesh, which may help explore and foster nature-inspired rational drug discovery.
Identifiants
pubmed: 34844546
pii: MC-EPUB-119110
doi: 10.2174/1573406418666211129103458
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
724-733Subventions
Organisme : Bangladesh Council of Scientific and Industrial Research, by the ministry of science and technology, People’s Republic of Bangladesh
ID : 39.02.0000.11.014.007.2017/1358
Informations de copyright
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