title: QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery.
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.
authors: Centro de Química Fina e Biotecnologia (CQFB)/LAQV-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus Caparica, Caparica 2829-516, Portugal. florbela.pereira@fct.unl.pt.
authors: Florbela
authors: F
authors: Pereira
authors: Centro de Química Fina e Biotecnologia (CQFB)/LAQV-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus Caparica, Caparica 2829-516, Portugal. dalatino@fc.ul.pt.|Centro de Ciências Moleculares e Materiais (CCMM), Departamento de Química e Bioquímica, Faculdade de Ciências, Universida Lisboa, Campo Grande, Lisboa 1749-016, Portugal. dalatino@fc.ul.pt.
authors: Diogo A R S
authors: DA
authors: Latino
authors: Centro de Química Fina e Biotecnologia (CQFB)/LAQV-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus Caparica, Caparica 2829-516, Portugal. s.gaudencio@fct.unl.pt.
authors: Susana P
authors: SP
authors: Gaudêncio
mesh_terms: D000900:Anti-Bacterial Agents / Q000737:chemistry / Q000302:isolation & purification* / Q000494:pharmacology; D000970:Antineoplastic Agents / Q000737:chemistry / Q000302:isolation & purification* / Q000494:pharmacology; D059001:Aquatic Organisms / Q000737:chemistry; D001688:Biological Products / Q000737:chemistry / Q000302:isolation & purification* / Q000494:pharmacology; D062126:Databases, Chemical; D055808:Drug Discovery / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods; D000069550:Machine Learning; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000900:Anti-Bacterial Agents; D000970:Antineoplastic Agents; D001688:Biological Products