Artificial neural networks applied to potentiometric titration of multi-component polybasic acid mixtures

A three-layer artificial neural network model with back-propagation of error is used to treat potentiometric acid-base titration data for estimating the concentrations of individual components in polybasic weak acid mixtures. The network's architecture and parameters were optimized and an empirical rule for dynamically adjusting the learning rate is put forward to improve the network's performance. Satisfactory prediction results were obtained for three-component samples containing maleic acid, propandioic acid and succinic acid with an average relative error of 4.5%.     

Hypervalent iodine(III)-induced intramolecular cyclization of alpha-(aryl)alkyl-beta-dicarbonyl compounds: a convenient synthesis of benzannulated and spirobenzannulated compounds.

A novel hypervalent iodine(III)-induced direct intramolecular cyclization of alpha-(aryl)alkyl-beta-dicarbonyl compounds has been described. Both meta- and para-substituted phenol ether derivatives containing acyclic or cyclic 1,3-dicarbonyl moieties at the side chain undergo this reaction in a facile manner. The reactions afford benzannulated and spirobenzannulated compounds that are of biological importance. The reaction is found to be general, mild, and high yielding. The mechanism of the reaction has been shown to involve a cation radical intermediate.     

Application of artificial neural network to determination of active principle ingredient in pharmaceutical quality control based on near infrared spectroscopy

In the construction of a neural network, most attentions have been paid to the selection of the architecture, the selection of the learning parameters and the network validation while the selection of input variables shared little. This study focused on the selection of input variables by various data pre-treatment for constructing ANN models. The results showed that the validation results differed from each other when different data-pretreatment methods combined with near-infrared spectroscopy (NIRS) to build a model using artificial neural network (ANN) for quality control of paracetamol in coldrex. And wavelet coefficients after orthogonal signal correction (OSC) in the ANN models reduced RMSEP by up to 77% compared to ANN models using derivatives combined with PCA pretreatment. The selection of input variables has potent to improve the calibration ability of ANN, and the model can be used for pressure reduction of quality control in the pharmaceutical industry.     

Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

Multicomponent acid-base titration by principal component-artificial neural network calibration

In this study, a three-layered feed-forward artificial neural network (ANN) trained by back-propagation learning was used to model the complex non-linear relationship between the concentration of anthranilic acid (HA), nicotinic acid (HN), picolinic acid (HP) and sulfanilic acid (HS) in their quaternary mixtures and the pH of solutions at different volumes of the added titrant. The principal components of the pH matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of acids in synthetic mixtures. The results showed that the ANN used can proceed the titration data with low percent relative error of prediction (i.e.<4%). A comparison between the ANN and PLS methods revealed the superiority of the results obtained by the former method.     

Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds

The multilayer feed-forward ANN is an important modeling technique used in QSAR studying. The training of ANN is usually carried out only to optimize the weights of the neural network and without paying attention to the network topology. Some other strategies used to train ANN are, first, to discover an optimum structure of the network, and then to find weights for an already defined structure. These methods tend to converge to local optima, and may also lead to overfitting. In this article, a hybridized particle swarm optimization (PSO) approach was applied to the neural network structure training (HPSONN). The continuous version of PSO was used for the weight training of ANN, and the modified discrete PSO was applied to find appropriate the network architecture. The network structure and connectivity are trained simultaneously. The two versions of PSO can jointly search the global optimal ANN architecture and weights. A new objective function is formulated to determine the appropriate network architecture and optimum value of the weights. The proposed HPSONN algorithm was used to predict carcinogenic potency of aromatic amines and biological activity of a series of distamycin and distamycin-like derivatives. The results were compared to those obtained by PSO and GA training in which the network architecture was kept fixed. The comparison demonstrated that the HPSONN is a useful tool for training ANN, which converges quickly towards the optimal position, and can avoid overfitting in some extent.     

Synthetische Juvenilhormone 1. Mitteilung. p-Substituierte β-Methyl-Zimtsäurederivate

In the first communication the synthetic methods are described for preparing aromatic analogues of juvenile hormone like activity. The para-substituted 2-methyl-cinnamic acid derivatives are formed by a Friedel-Crafts acylation of a phenol, followed by the Wadsworth-Emmons (Wittig-Horner)-Reaction with the corresponding phosphonates. More than 120 compounds are listed.     

Predicting the standard enthalpy (deltaH0f) and entropy (S0) of alkanes by artificial neural networks

Artificial Neural Networks (ANNs) with Extended Delta-Bar-Delta (EDBD) back propagation learning algorithm have been developed to predict the standard enthalpy and entropy of 87 acyclic alkanes. Molecular weight, boiling point and density of the compounds were used as input parameters. The network's architecture and parameters were optimized to give maximum performances. The best network was a 3-6-2 ANN, and the optimum learning epoch was about 1320. The results show that the maximum relative errors of enthalpy and entropy are less than 3%. They reveal that the performances of ANNs for predicting the enthalpy and entropy of alkanes are satisfying.     

Pharmacological classification of drugs based on neural network processing of molecular modeling data

The performance of artificial neural network (ANN) models in predicting pharmacological classification of structurally diverse drugs based on their theoretical chemical parameters was demonstrated. The classification coefficients for psychotropic agents, beta-adrenolytic drugs, histamine H(1) receptor antagonists and drugs binding to alpha-adrenoceptors were 100, 100, 95 and 86%, respectively. A set of easily accessible non-empirical molecular parameters describing the structure of xenobiotics can provide information allowing the prediction of some pharmacological properties of drugs and drug candidates employing ANN models. Since ANN analysis can help cluster as well as segregate drugs and drug candidates according to their known and expected pharmacological properties, the number of routine biological assays might be reduced. The results presented here might be used to improve the efficiency of high throughput screening programs for new drug hits by demonstrating a promising procedure for diverse combinatorial library design and evaluation.