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

Zheng, Fang; Zheng, Guangrong; Deaciuc, Agripina G.; Zhan, Chang‐Guo; Dwoskin, Linda P.; Crooks, Peter A.

doi:10.1080/14756360801945648

Cited by 7 publications

(8 citation statements)

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“…19–21,33–38 This artificial system emulates brain function, in which a very high number of information-processing neurons are interconnected and are known for their ability to model a wide set of functions, including linear and non-linear functions, without knowing the analytic forms in advance. Being different from a linear model, neural network prediction can be expected in a pre-specified data range.…”

Section: Resultsmentioning

confidence: 99%

“…In construction of the initial molecular structures, a formal charge of +1 was assigned to each positively charged nitrogen atom in the structures of these compounds, and the alkyl chain connecting the head group(s) was kept in its fully extended conformation. 19,21 The geometry optimization was first performed using the molecular mechanics (MM) method with the Tripos force field and the default convergence criterion, which was then followed by a semi-empirical molecular orbital (MO) energy calculation at the PM3 level. 39 …”

Section: Methodsmentioning

confidence: 99%

“…19,21 Starting from the entire set of descriptors, variable selection by a forward and reverse stepwise regression procedure was performed, in which forward selection was followed by backward elimination of variables, resulting in an equation in which only variables that significantly increased the predictability of the dependent variable were included.…”

Section: Methodsmentioning

confidence: 99%

“…19,21,34 Network weights ( W ji (s)) for a neuron ‘ j ’ receiving output from neuron ‘ i ’ in the layer ‘ s ’ were initially assigned random values between −0.5 and +0.5. The sigmoidal function was chosen as the transfer function that generates the output of a neuron from the weighted sum of inputs from the preceding layer of units.…”

Section: Methodsmentioning

confidence: 99%

“…15–18 In addition, the structure activity/function relationships of the novel quaternary ammonium nAChR antagonists have been studied previously using various QSAR modeling approaches. 19–21 …”

Section: Introductionmentioning

confidence: 99%

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QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(−)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

Zheng

McConnell

Chen

et al. 2009

Bioorganic & Medicinal Chemistry

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Maximal inhibition (Imax) of the agonist effect is an important pharmacological property of inhibitors that interact with multiple receptor subtypes that are activated by the same agonist and which elicit the same functional response. This report represents the first QSAR study on a set of 66 mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating nicotine-evoked dopamine release, conducted using multi-linear regression (MLR) and neural network (NN) analysis with the maximal inhibition (Imax) values of the antagonists as target values. The statistical results for the generated MLR model were: r2 = 0.89, rmsd = 9.01, q2 = 0.83 and loormsd = 11.1; the statistical results for the generated NN model were: r2 = 0.89, rmsd = 8.98, q2 = 0.83 and loormsd = 11.2. The maximal inhibition values of the compounds exhibited a good correlation with the predictions made by the QSAR models developed, which provide a basis for rationalizing selection of compounds for synthesis in the discovery of effective and selective second generation inhibitors of nAChRs mediating nicotine-evoked dopamine release.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(−)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

Zheng

McConnell

Chen

et al. 2009

Bioorganic & Medicinal Chemistry

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Classification and Prediction

Sullivan¹

2011

Introduction to Data Mining for the Life Sciences

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under analysis. In the context of classification, data tuples can be referred to as samples, examples, instances, data points, or objects. 2 Since the class label of each training tuple is provided, this step is also known as supervised learning (i.e., the learning of the classifier is "supervised" in that it is told to which class each training tuple belongs). It contrasts with unsupervised learning (or clustering), in which the class label of each training tuple is not known, and the number or set of classes to be learned may not be known in advance. For example, if we did not have the loan decision data available for the training set, we could use clustering to try to determine "groups of like tuples", which may correspond to risk groups within the loan application data. Clustering is the topic of Chapter 7. This first step of the classification process can also be viewed as the learning of a mapping or function, y = f (X), that can predict the associated class label y of a given tuple X. In this view, we wish to learn a mapping or function that separates the data classes. Typically, this mapping is represented in the form of classification rules, decision trees, or mathematical formulae. In our example, the mapping is represented as classification rules that identify loan applications as being either safe or risky (Figure 6.1(a)). The rules can be used to categorize future data tuples, as well as provide deeper insight into the database contents. They also provide a compressed representation of the data. "What about classification accuracy?" In the second step (Figure 6.1(b)), the model is used for classification. First, the predictive accuracy of the classifier is estimated. If we were to use the training set to measure the accuracy of the classifier, this estimate would likely be optimistic since the classifier tends to overfit the data (that is, during learning it may incorporate some particular anomalies of the training data that are not present in the general data set overall). Therefore, a test set is used, made up of test tuples and their associated class labels. These tuples are randomly selected from the general data set. They are independent of the training tuples, meaning that they are not used to construct the classifier. The accuracy of a classifier on a given test set is the percentage of test set tuples that are correctly classified by the classifier. The associated class label of each test tuple is compared with the learned classifier's class prediction for that tuple. Section 6.13 describes several methods for estimating classifier accuracy. If the accuracy of the classifier is considered acceptable, the classifier can be used to classify future data tuples for which the class label is not known. (Such data are also referred to in the machine learning literature as "unknown" or "previously unseen" data.) For example, the classification rules learned in Figure 6.1(a) from the analysis of data from previous loan applications can be used to approve or reject new or future loan applicants. "How i...

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First-principles determination of molecular conformations of indolizidine (−)-235B′ in solution

et al. 2009

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Indolizidine (−)-235B′ is a particularly interesting natural product, as it is the currently known, most potent and subtype-selective open-channel blocker of the α4β2 nicotinic acetylcholine receptor (nAChR). In the current study, extensive first-principles electronic structure calculations have been carried out in order to determine the stable molecular conformations and their relative free energies of the protonated and deprotonated states of (−)-235B′ in the gas phase, in chloroform, and in aqueous solution. The 1 H and 13 C NMR chemical shifts calculated using the computationally determined dominant molecular conformation of the deprotonated state are all consistent with available experimental NMR spectra of (−)-235B′ in chloroform, which suggests that the computationally determined molecular conformations are reasonable. Our computational results reveal for the first time that two geminal H atoms on carbon-3 (C3) of (−)-235B′ have remarkably different chemical shifts (i.e. 3.24 and 2.03 ppm). The computational results help one to better understand and analyze the experimental 1 H NMR spectra of (−)-235B′. The finding of remarkably different chemical shifts of two C3 geminal H atoms in a certain molecular conformation of (−)-235B′ may also be valuable in analysis of NMR spectra of other related ring-containing compounds. In addition, the pK a of (−)-235B′ in aqueous solution is predicted to be ~9.7. All of the computational results provide a solid basis for future studies of the microscopic and phenomenological binding of various receptor proteins with the protonated and deprotonated structures of this unique open-channel blocker of α4β2 nAChRs. This computational study also demonstrates how one can appropriately use computational modeling and spectroscopic analysis to address the structural and spectroscopic problems that cannot be addressed by experiments alone.

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

Cited by 7 publications

References 33 publications

QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(−)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

QSAR study on maximal inhibition (Imax) of quaternary ammonium antagonists for S-(−)-nicotine-evoked dopamine release from dopaminergic nerve terminals in rat striatum

Classification and Prediction

First-principles determination of molecular conformations of indolizidine (−)-235B′ in solution

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