Cheminformatics to Characterize Pharmacologically Active Natural Products
Abstract
:1. Introduction
2. Natural Product Databases
3. Chemoinformatic Profiling
3.1. Physicochemical Properties
3.2. Molecular Scaffolds
3.3. Molecular Complexity
3.4. Fragments
3.5. Acid and Base Profiling
3.6. ADME/Tox Profiling
3.7. Global Diversity
3.8. Chemical Space: Visual Representation
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data Set | Goal and Approach | Reference |
---|---|---|
454 NP from Panama. | Build and characterize the contents and diversity of a NP collection from Panama. Comparison with NP from other geographical regions. | [24,25] |
560 cyanobacteria metabolites (freshwater and marine). | Quantify the distribution of drug-like properties; measure the diversity using properties, molecular fingerprints, and molecular scaffolds. | [26] |
209,574 compounds from the Universal Natural Products Database and other NPs. | Comparative analysis of molecular complexity diversity based on physicochemical properties, molecular scaffolds and fingerprints. Comparison with drugs approved for clinical use. | [23,27] |
209,574 compounds from the Universal Natural Products Database, 423 molecules from BIOFACQUIM and other NPs. | Comparative analysis of the acid/based profile of NP from different sources. Comparison with drugs approved for clinical use and food chemicals. | [28] |
503 NPs from Mexico collected in the BIOFACQUIM database. | Diversity analysis based on different molecular representations and ADME/Tox profiling. | [29,30] |
578 compounds from honey bee and stingless bee propolis. | Analysis of chemical space, chemical diversity, and scaffold content. | [31] |
897 metabolites from the Seaweed Metabolite Database (SWMD). | Diversity analysis based on different molecular representations. | [32] |
1870 compounds from the Eastern Africa Natural Product Database (EANPDB). | Quantification of scaffold diversity and profiling of drug-likeness and ADME/Tox properties. | [19] |
NPs from four NP data sets: phytochemica, SerpentinaDB, SANCDB, and NuBBEDB. | In silico profiling of ADME/Tox properties. | [33] |
6524 NPs originating from about 3300 producer streptomycetes strains | In addition to names and molecular structures of the compounds, information about source organisms, references, biological role, activities, synthesis routes, scaffolds, physicochemical properties, and predicted ADMET properties is included. | [34] |
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Medina-Franco, J.L.; Saldívar-González, F.I. Cheminformatics to Characterize Pharmacologically Active Natural Products. Biomolecules 2020, 10, 1566. https://doi.org/10.3390/biom10111566
Medina-Franco JL, Saldívar-González FI. Cheminformatics to Characterize Pharmacologically Active Natural Products. Biomolecules. 2020; 10(11):1566. https://doi.org/10.3390/biom10111566
Chicago/Turabian StyleMedina-Franco, José L., and Fernanda I. Saldívar-González. 2020. "Cheminformatics to Characterize Pharmacologically Active Natural Products" Biomolecules 10, no. 11: 1566. https://doi.org/10.3390/biom10111566
APA StyleMedina-Franco, J. L., & Saldívar-González, F. I. (2020). Cheminformatics to Characterize Pharmacologically Active Natural Products. Biomolecules, 10(11), 1566. https://doi.org/10.3390/biom10111566