A comparison of quantitative structure-activity relationships for the effect of benzoic and cinnamic acids on Listeria monocytogenes using multiple linear regression,artificial neural network and fuzzy systems

The ability of artificial neural networks (ANN), fuzzy systems (FS) and multiple linear regression (MLR) to fit the biological activity surface describing the inhibition of Listeria monocytogenes by benzoic and cinnamic acid derivatives was compared. MLR and ANN were also compared for their ability to select the properties that best describe the biological activity of the compounds. The criteria used for comparing surface fits of all models were the coefficient of determination r2 and the standard deviation of the error, s(e). The ANN method gave a better correlation, r2 = 0.96, compared with either MLR, r2 = 0.81, or FS, r2 = 0.92, and also a lower standard error, possibly indicating non-linearity in the data. The ANN was shown to generalize better than MLR using the leave-one-out method. The ANN selection algorithm for the selection of the parameters that contributed most to the biological activity of the phenols (log K and pKa) agreed with the selected parameters of the MLR system.     

2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks

Negative inotropic potency of 60 benzothiazepine-like calcium entry blockers (CEBs), Diltiazem analogs, was successfully modeled using Bayesian-regularized genetic neural networks (BRGNNs) and 2D autocorrelation vectors. This approach yielded reliable and robust models whilst by means of a linear genetic algorithm (GA) search routine no multilinear regression model was found describing more than 50% of the training set. On the contrary, the optimum neural network predictor with five inputs described about 84% and 65% variances of 50 randomly selected training and test sets. Autocorrelation vectors in the nonlinear model contained information regarding 2D spatial distributions on the CEB structure of van der Waals volumes, electronegativities, and polarizabilities. However, a sensitivity analysis of the network inputs pointed out to the electronegativity and polarizability 2D topological distributions at substructural fragments of sizes 3 and 4 as the most relevant features governing the nonlinear modeling of the negative inotropic potency.     

Computational neural network analysis of the affinity of N-n-alkylnicotinium salts for the alpha4beta2* nicotinic acetylcholine receptor

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 [(3)H]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 C(4) (n-butyl) analogue. A decrease in K(i) value between C(12) and C(13) was also observed. The statistical results for the best neural network fit of the 85 experimental K(i) values are r(2) = 0.84, rmsd = 0.39; r(cv)(2) = 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.     

Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques

• 1 Multiple linear regression (MLR), generalised additive models (GAM) and artificial neural networks (ANN), were used to define young of the year (0+) roach (Rutilus rutilus) microhabitat and to predict its abundance.
• 2 0+ Roach and nine environmental variables were sampled using point abundance sampling by electrofishing in the littoral area of Lake Pareloup (France) during summer 1997. Eight of these variables were used to set up the models after log₁₀ (x+ 1) transformation of the dependent variable (0+ roach density). Model training and testing were performed on independent subsets of the whole data matrix containing 306 records.
• 3 The predictive quality of the models was estimated using the determination coefficient between observed and estimated values of roach densities. The best models were provided by ANN, with a correlation coefficient (r) of 0.83 in the training procedure and 0.62 in the testing procedure. GAM and MLR gave lower prediction in the training set (r = 0.53 for GAM and r = 0.32 for MLR) and in the testing set (r = 0.48 for GAM and r = 0.43 for MLR). In the same way, samples without fish were reliably predicted by ANN whereas GAM and MLR predicted absence unreliably.
• 4 ANN sensitivity analysis of the eight environmental variables in the models revealed that 0+ roach distribution was mainly influenced by five variables: depth, distance from the bank, local slope of the bottom and percentage of mud and flooded vegetation cover. The nonlinear influence of these variables on 0+ roach distribution was clearly shown using nonparametric lowess smoothing procedures.
• 5 Non‐linear modelling methods, such as GAM and ANN, were able to define 0+ fish microhabitat precisely and to provide insight into 0+ roach distribution and abundance in the littoral zone of a large reservoir. The results showed that in lakes, 0+ roach microhabitat is influenced by a complex combination of several environmental variables acting mainly in a nonlinear way.