Computational neural network analysis of the affinity of N-n-alkylnicotinium salts for the α4β2* nicotinic acetylcholine receptor

Abstract

Based on an 85 molecule database, linear regression with different size datasets and an artificial neural network approach have been used to build mathematical relationships to fit experimentally obtained affinity values (K_i) of a series of mono- and bis-quaternary ammonium salts from [³H]nicotine binding assays using rat striatal membrane preparations. The fitted results were then used to analyze the pattern among the experimental K_i values of a set of N-n-alkylnicotinium analogs with increasing n-alkyl chain length from 1 to 20 carbons. The affinity of these N-n-alkylnicotinium compounds was shown to parabolically vary with increasing numbers of carbon atoms in the n-alkyl chain, with a local minimum for the C₄ (n-butyl) analogue. A decrease in K_i value between C₁₂ and C₁₃ was also observed. The statistical results for the best neural network fit of the 85 experimental K_i values are r² = 0.84, rmsd = 0.39; r_cv² = 0.68, and loormsd = 0.56. The generated neural network model with the 85 molecule training set may also be of value for future predictions of K_i values for new virtual compounds, which can then be identified, subsequently synthesized, and tested experimentally.

Keywords::

• N-n-alkylnicotinium salts
• nicotinic acetylcholine receptor
• binding affinity
• neural network
• linear regression

Acknowledgement

This work was supported by NIH grant U19DA017548.