In this study, we developed several QSAR models based on simple descriptors (such as topological and constitutional) to estimate butyrylcholinesterase (BChE) inhibition potency, p (or pIC), of a set of 297 (289 after exclusion of outliers) structurally different compounds. The models were similar to the best model that we obtained previously for acetylcholinesterase AChE and were based on the valence molecular connectivity indices of second and third order ( and ), the number of aliphatic hydroxyl groups (nOH), AlogP Ghose-Crippen octanol-water partition coeff. (logP), and O-060-atom-centred fragments (Al-O-Ar, Ar-O-Ar, R..O..R and R-O-C=X). The best models with two and three descriptors yielded = 0.787 and S.E. = 0.89, and = 0.827 and S.E. = 0.81, respectively. We also correlated nine scoring functions, calculated for 20 ligands whose complexes with BChE we found in the Protein Data Bank as crystal structures to p (or pIC). The best correlations yielded PLP1 and PLP2 (Piecewise Linear Pairwise potential functions) with = 0.619 and 0.689, respectively. Correlation with certain simple topological and constitutional descriptors yielded better results, e.g., ( = 0.730), on the same set of compounds ( = 20).
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http://dx.doi.org/10.3390/molecules27206894 | DOI Listing |
Mol Phylogenet Evol
February 2025
Department of Entomology, National Museum of Natural History, Smithsonian Institution, 1000 Constitution Avenue NW, Washington, DC 20560, USA.
Tropical wandering spiders (Ctenidae) are a diverse family of cursorial predators whose species richness peaks in the tropics. The phylogeny of Ctenidae has been examined using morphology and Sanger-based sequencing data, but these studies have been limited by taxon sampling and have often recovered low branch support for many intrafamilial phylogenetic relationships. Herein, we present the most extensive phylogenetic sampling of this family using genome-scale data, leveraging museum collections of all ctenid subfamilies from across the world.
View Article and Find Full Text PDFJ Diabetes Res
November 2024
National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
This study elucidated the mechanistic role of (CR) in type 2 diabetes mellitus (T2DM) through bioinformatics analysis and experimental validation. Components and targets of CR were retrieved from the traditional Chinese medical systems pharmacology, while potential T2DM targets were obtained from GeneCards and Online Mendelian Inheritance in Man databases. Intersecting these datasets yielded target genes between CR and T2DM.
View Article and Find Full Text PDFAnal Chem
August 2024
Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, 500 05 Hradec Králové, Czechia.
The retention behavior in supercritical fluid chromatography and its stability over time are still unsatisfactorily explained phenomena despite many important contributions in recent years, especially focusing on linear solvation energy relationship modeling. We studied polar stationary phases with predominant -OH functionalities, i.e.
View Article and Find Full Text PDFACS Catal
June 2024
Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often nontransferable and cannot be applied across different reactions.
View Article and Find Full Text PDFJ Chem Theory Comput
June 2024
Department of Chemistry, New York University, New York, New York 10003, United States.
Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation.
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