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).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610766PMC
http://dx.doi.org/10.3390/molecules27206894DOI Listing

Publication Analysis

Top Keywords

topological constitutional
8
descriptors yielded
8
evaluation key
4
key structural
4
structural features
4
features butyrylcholinesterase
4
butyrylcholinesterase inhibitors
4
inhibitors simple
4
simple molecular
4
descriptors
4

Similar Publications

Reassessing the evolutionary relationships of tropical wandering spiders using phylogenomics: A UCE-based phylogeny of Ctenidae (Araneae) with the discovery of a new lycosoid family.

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 PDF

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 PDF

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 PDF

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 PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!