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 (Ki) 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 Ki 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 Ki value between C12 and C13 was also observed. The statistical results for the best neural network fit of the 85 experimental Ki values are r2 = 0.84, rmsd = 0.39; rcv2 = 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 Ki values for new virtual compounds, which can then be identified, subsequently synthesized, and tested experimentally.
|Number of pages
|Journal of Enzyme Inhibition and Medicinal Chemistry
|Published - 2009
Bibliographical noteFunding Information:
This work was supported by NIH grant U19DA017548.
- Binding affinity
- Linear regression
- N-n-alkylnicotinium salts
- Neural network
- Nicotinic acetylcholine receptor
ASJC Scopus subject areas
- Drug Discovery