Many heterocyclic amines (HCA) present in cooked food exert a genotoxic activity when they are metabolised (N-oxidated) by the human cytochrome P450 1A2 (CYP1A2h). In order to rationalize the observed differences in activity of this enzyme on a series of 12 HCA, 3D-QSAR methods were applied on the basis of models of HCA-CYP1A2h complexes. The CYP1A2h enzyme model has been previously reported and was built by homology modeling based on cytochrome P450 BM3. The complexes were automatically generated applying the AUTODOCK software and refined using AMBER. A COMBINE analysis on the complexes identified the most important enzyme-ligand interactions that account for the differences in activity within the series. A GRID/GOLPE analysis was then performed on just the ligands, in the conformations and orientations found in the modeled complexes. The results from both methods were concordant and confirmed the advantages of incorporating structural information from series of ligand-receptor complexes into 3D-QSAR methodologies.
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http://dx.doi.org/10.1023/a:1008164621650 | DOI Listing |
Curr Top Med Chem
January 2025
Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research (JSS AHER), Mysuru, Karnataka, India.
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Research and Development Center, Bioinnov Solutions LLP, Salem, India.
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View Article and Find Full Text PDFBMC Chem
January 2025
Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia.
Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS.
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Pharmacy Department, CEU Cardenal Herrera University, CEU Universities C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain.
In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology.
View Article and Find Full Text PDFJ Fungi (Basel)
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Department of Biosciences and Biotechnologies, Graduate School of Bioresources and Bioenvironment Sciences, Kyushu University, 744 W5-674, Motooka Nishi-ku, Fukuoka 819-0395, Japan.
(), the second most prevalent Candida pathogen globally, has emerged as a major clinical threat due to its ability to develop high-level azole resistance. In this study, two new 5,6-dihydrotetrazolo[1,5-]quinazoline derivatives ( and ) were synthesized and characterized using IR, LC-MS, H, and C NMR spectra. Along with 13 previously reported analogues, these compounds underwent in vitro antifungal testing against clinical isolates using a serial dilution method (0.
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