Design, Synthesis, Antimicrobial Properties, and Molecular Docking of Novel Furan-Derived Chalcones and Their 3,5-Diaryl-∆2-pyrazoline Derivatives.

The present work focuses on the synthesis and preliminary structure activity relationships (SARs) of furan-derived chalcones and their corresponding ∆2-pyrazoline derivatives as antimicrobial agents. Eight novel chalcone derivatives and eight ∆2-pyrazoline compounds were synthesized in moderate to good isolated yields. The target compounds were evaluated as antimicrobial agents against two Gram-positive (Staphylococcus aureus and Staphylococcus epidermidis), two Gram-negative (Escherichia coli and Klebsiella pneumoniae), and fungi (Candida albicans) species. Based on the SARs, chalcones 2a and 2h showed inhibition activity on all tested microbial species, while ∆2-pyrazoline 3d was found to be selective for some microbial species. The most potent compounds (2a, 2h, and 3d) were docked into glucosamine-6-phosphate synthase (GlcN-6-P), the molecular target enzyme for antimicrobial agents, utilizing the Autodock 4.2 program, in order to study their virtual affinity and binding mode with the target enzyme. The selected potent compounds were found to bind to the active site of the enzyme probably in a similar way to that of the substrate as suggested by the docking study. In summary, the newly developed furan-derived chalcones and their ∆2-pyrazoline derivatives could serve as potent leads toward the development of novel antimicrobial agents.

Discovery of new ß-D-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening.

Glycosidases, including ß-D-glucosidase, are involved in a variety of metabolic disorders such as diabetes, viral or bacterial infections and cancer. Accordingly, we were prompted to find new ß-D-glucosidase inhibitors. Towards this end we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Three pharmacophores emerged in the QSAR equations, suggesting the existence of more than one binding mode accessible to ligands within the ß-D-glucosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic (ROC) curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were established experimentally by the identification of several ß-D-glucosidase inhibitors retrieved via in silico search of two structural databases, namely the National Cancer Institute (NCI) list of compounds, and our in-house structural database of established drugs and agrochemicals (DAC).

Discovery of new ß-D-galactosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening.

Glycosidases, including ß-D-galactosidase, are involved in a variety of metabolic disorders, such as diabetes, viral or bacterial infections, and cancer. Accordingly, we were prompted to find new ß-D-galactosidase inhibitors. Towards this end, we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were used to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Five pharmacophores emerged in the QSAR equations suggesting the existence of more than one binding mode accessible to ligands within ß-D-galactosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were experimentally established by the identification of several ß-D-galactosidase inhibitors retrieved via in silico search of two structural databases: the National Cancer Institute list of compounds and our in house built structural database of established drugs and agrochemicals.