DiaNat-DB-v2: A Molecular Database of Antidiabetic Compounds from Medicinal Plants and Functional Foods
Diabetes mellitus continues to be a significant health problem worldwide, and effective strategies and resources for its prevention and treatment are needed. Here, we present DiaNat-DB-v2, a refined and expanded version of the compound database that incorporates newly identified antidiabetic compounds from medicinal plants. By incorporating food-derived compounds, DiaNat-DB-v2 bridges the gap between medicinal chemistry and functional food research, creating new opportunities for nutraceutical development, personalized dietary interventions, and drug discovery. A comprehensive analysis of structural content, diversity, chemical space coverage, and safety-related metrics revealed that the updated database exhibits minimal overlap with FDA-approved drugs and contains a large proportion of unique molecular scaffolds, underscoring its originality and complementarity. Furthermore, the database has high natural-product likeness, a low incidence of structural alerts, and moderate compliance with classical druglikeness criteria. Together, these findings emphasize the value of DiaNat-DB-v2 as a resource for both health-oriented compound discovery and nutrition-focused research. DiaNat-DB-v2 is freely accessible as Supporting Information.
Authors
- Mehdi D. Davari (ORCID: https://orcid.org/0000-0003-0089-7156)
- Berenice Ovalle‐Magallanes (ORCID: https://orcid.org/0000-0002-4774-722X)
- Abraham Madariaga‐Mazón (ORCID: https://orcid.org/0000-0002-8938-1318)
- Karina Martínez‐Mayorga (ORCID: https://orcid.org/0000-0002-6974-7941)
- David Ramírez‐Palma (ORCID: https://orcid.org/0000-0003-3800-6611)
- José L. Medina‐Franco (ORCID: https://orcid.org/0000-0003-4940-1107)
- Juan F. Avellaneda-Tamayo (ORCID: https://orcid.org/0009-0003-1819-6187)
- Jimena García-Vázquez
- Nancy De Jesús-Reyes
Institutions
- Leibniz Institute of Plant Biochemistry (DE)
- Autonomous University of Yucatán (MX)
- Universidad Nacional Autónoma de México (MX)
Publication Details
- Journal
- ACS Omega
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1021/acsomega.5c09685
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00