Evaluation of the Key Structural Features of Various Butyrylcholinesterase Inhibitors Using Simple Molecular...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Butyrylcholinesterase (BChE) is a key target for treating Alzheimer's disease and other neurological disorders.
• Developing effective BChE inhibitors requires understanding the relationship between chemical structure and inhibitory activity.
• Existing methods for predicting BChE inhibition can be computationally intensive or lack accuracy.

Purpose of the Study:

• To develop and validate Quantitative Structure-Activity Relationship (QSAR) models for predicting butyrylcholinesterase (BChE) inhibition potency.
• To identify key molecular descriptors that correlate with BChE inhibitory activity.
• To compare the predictive performance of QSAR models with scoring functions derived from protein-ligand complexes.

Main Methods:

• Development of QSAR models using simple topological and constitutional descriptors, including valence molecular connectivity indices (χ), number of hydroxyl groups (nOH), and Ghose-Crippen octanol-water partition coefficient (logP).
• Calculation of nine scoring functions for 20 ligands with known BChE crystal structures.
• Correlation analysis between calculated descriptors/scoring functions and experimental BChE inhibition potency (pKi or pIC50).

Main Results:

• The best QSAR models with two and three descriptors achieved correlation coefficients (r) of 0.787 and 0.827, respectively.
• Piecewise Linear Pairwise (PLP) potential functions (PLP1 and PLP2) showed moderate correlations (r = 0.619 and 0.689) with BChE inhibition.
• A single descriptor, the third-order valence molecular connectivity index (³χ), demonstrated a strong correlation (r = 0.730) with BChE inhibition for a subset of 20 compounds.

Conclusions:

• Simple molecular descriptors are effective for building accurate QSAR models to predict BChE inhibition potency.
• QSAR models based on topological and constitutional descriptors offer a more reliable approach than scoring functions for this specific dataset.
• These findings can guide the design of novel BChE inhibitors with improved potency and selectivity.