QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release

Back-propagation artificial neural networks (ANNs) were trained on a dataset of 42 molecules with quantitative IC50 values to model structure–activity relationships of mono- and bis-quaternary ammonium salts as antagonists at neuronal nicotinic acetylcholine receptors (nAChR) mediating nicotine-evoked dopamine release. The ANN QSAR models produced a reasonable level of correlation between experimental and calculated log (1/IC50) (r2 = 0.76, rcv2=0.64). An external test for the models was performed on a dataset of 18 molecules with IC50 values >1 μM. Fourteen of these were correctly classified. Classification ability of various models, including self-organizing maps (SOM), for all 60 molecules was also evaluated. A detailed analysis of the modeling results revealed the following relative contributions of the used descriptors to the trained ANN QSAR model: ∼44.0% from the length of the N-alkyl chain attached to the quaternary ammonium head group, ∼20.0% from Moriguchi octanol–water partition coefficient of the molecule, ∼13.0% from molecular surface area, ∼12.6% from the first component shape directional WHIM index/unweighted, ∼7.8% from Ghose–Crippen molar refractivity, and 2.6% from the lowest unoccupied molecular orbital energy. The ANN QSAR models were also evaluated using a set of 13 newly synthesized compounds (11 biologically active antagonists and two biologically inactive compounds) whose structures had not been previously utilized in the training set. Twelve among 13 compounds were predicted to be active which further supports the robustness of the trained models. Other insights from modeling include a structural modification in the bis-quinolinium series that involved replacing the 5 and/or 8 as well as the 5′ and/or 8′ carbon atoms with nitrogen atoms, predicting inactive compounds. Such data can be effectively used to reduce synthetic and in vitro screening activities by eliminating compounds of predicted low activity from the pool of candidate molecules for synthesis. The application of the ANN QSAR model has led to the successful discovery of six new compounds in this study with experimental IC50 values of less than 0.1 μM at nAChR subtypes responsible for mediating nicotine-evoked dopamine release, demonstrating that the ANN QSAR model is a valuable aid to drug discovery.

Back-propagation artificial neural networks were trained to model structure–activity relationships of mono- and bis-quaternary ammonium salts for the neural nicotinic acetylcholine receptor subtypes responsible for mediating nicotine-evoked dopamine release. A detailed analysis of the modeling results reveals relative contributions of the used descriptors to the trained ANN QSAR model, that is, ∼44.0% from the length of the alkyl chain attached to a molecular head group, ∼20.0% from Moriguchi octanol–water partition coefficient of the molecule, ∼13.0% from molecular surface area, ∼12.6% from the first component shape directional WHIM index/unweighted, ∼7.8% from Ghose–Crippen molar refractivity, and 2.6% from the lowest unoccupied molecular orbital energy.Figure optionsDownload as PowerPoint slide