ArticleMolecules (Basel, Switzerland)2015
QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery.
Article in Molecules (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Machine learning-based quantitative structure-activity relationship model for antibiotic prediction and discovery.PLOS digital health · 2026Article
- Conceptual density functional theory in drug discovery: an overview.Journal of molecular modeling · 2025Review
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
- Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products.ACS synthetic biology · 2023Review
- Review
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening.International journal of molecular sciences · 2021Article
- Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products.Frontiers in chemistry · 2021Review
- A Structure- and Ligand-Based Virtual Screening of a Database of "Small" Marine Natural Products for the Identification of "Blue" Sigma-2 Receptor Ligands.Marine drugs · 2018Article
- In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.Biomolecules · 2018Article
- Review
- 2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors.Molecular diversity · 2017Article
- In Silico Screening of IL-1β Production Inhibitors Using Chemometric Tools.Iranian journal of pharmaceutical research : IJPR · 2017Article
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3 authors.
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Abstract
A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database-25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.
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