Data mining for seeking an accurate quantitative relationship between molecular structure and GC retention indices of alkenes by projection pursuit

Quantitative structure activity relationship modeling for predicting radiosensitization effectiveness of nitroimidazole compounds

This paper provides quantitative structure activity relationship (QSAR) models for predicting the radiosensitization effectiveness of nitroimidazole compounds. A new method, combining a heuristic method and projection pursuit regression, was used to build an advanced QSAR model. Compared to the conventional multi-linear regression model, this model showed better predictive ability and reliability, with the values of regression coefficient (R(2)) and root mean square error (RMSE) 0.92 and 0.18 for the training set and 0.90 and 0.17 for the test set, respectively. The provided models were useful tools to predict the radiosensitization effectiveness of nitroimidazole compounds. Also, the new finding descriptors derived from this study will help us to facilitate the design of new radiation sensitizers with better activities.

Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression

Three machine learning methods, genetic algorithm-multilinear regression (GA-MLR), least-squares support vector machine (LS-SVM), and project pursuit regression (PPR), were used to investigate the relationship between thiazoline derivatives and their fungicidal activities against the rice blast disease. The GA-MLR method was used to select the most appropriate molecular descriptors from a large set of descriptors, which were only calculated from molecular structures, and develop a linear quantitative structure-activity relationship (QSAR) model at the same time. On the basis of the selected descriptors, the other two more accurate models (LS-SVM and PPR) were built. Both the linear and nonlinear modes gave good prediction results, but the nonlinear models afforded better prediction ability, which meant that the LS-SVM and PPR methods could simulate the relationship between the structural descriptors and fungicidal activities more accurately. The results show that the nonlinear methods (LS-SVM and PPR) could be used as good modeling tools for the study of rice blast. Moreover, this study provides a new and simple but efficient approach, which should facilitate the design and development of new compounds to resist the rice blast disease.

Novel approaches to predict the retention of histidine-containing peptides in immobilized metal-affinity chromatography

The new method lazy learning method-local lazy regression (LLR) was first used to model the quantitative structure-retention relationship (QSRR) for predicting and explaining the retention behaviors of peptides in the nickel column in immobilized metal-affinity chromatography (IMAC). The best multilinear regression (BMLR) method implemented in the CODESSA was used to select the most appropriate molecular descriptors from a large set and build a linear regression model. Based on the selected five descriptors, another two approaches, projection pursuit regression (PPR) and LLR were used to build more accurate QSRR models. The coefficients of determination (R(2)) of the best model developed based on LLR were 0.9446 and 0.9252 for the training set and the test set, respectively. By comparison, it was proved that the novel local learning method LLR was a very promising tool for QSRR modeling with excellent predictive capability for the prediction of imidazole concentration (IMC) values of histidine-containing peptides in IMAC. It could be used in other chromatography research fields and that should facilitate the design and purification of peptides and proteins.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Quantitative structure and retention relationships for gas chromatographic data: Application to alkyl pyridines on apolar and polar phases

Quantitative structure and retention relationships (QSRR) have been developed to model gas chromatographic retention data of alkyl pyridines on apolar (branched alkane) and polar (primary alcohol) stationary phases. The retention properties analyzed are Kovats retention index, I; partial molar enthalpy, DeltaH; partial molar entropy, DeltaS and partition coefficient, log K. Using the seven valence molecular connectivity indices (chi) calculated for the 18 alkyl pyridines, regression models are generated to predict the retention properties. The best model (model A) obtained with the descriptors ((1)chi(P)(V), (3)chi(P)(V) and (6)chi(CH)(V) was unable to produce a satisfactory statistical performance and correct order of elution. The model has been modified (model B) by including steric parameter s, which has been empirically derived by considering the steric effects due to the presence of alkyl groups at the ortho and meta positions. The modified model predicts the correct order of elution for all the alkyl pyridines and good correlation coefficients, r. The r values obtained for I, DeltaH, DeltaS and log K are: r=0.955, 0.975, 0.984 and 0.955 (model A) become r=0.999, 0.996, 0.990 and 0.999 (model B) on the apolar stationary phase and r=0.931, 0.926, 0.911 and 0.931 (model A) become r=0.999, 0.992, 0.958 and 0.999 (model B) on the polar stationary phase.

The application of Kriging and empirical Kriging based on the variables selected by SCAD

The commonly used approach for building a structure-activity/property relationship consists of three steps. First, one determines the descriptors for the molecular structure, then builds a metamodel by using some proper mathematical methods, and finally evaluates the meta-model. Some existing methods only can select important variables from the candidates, while most metamodels just explore linear relationships between inputs and outputs. Some techniques are useful to build more complicated relationship, but they may not be able to select important variables from a large number of variables. In this paper, we propose to screen important variables by the smoothly clipped absolute deviation (SCAD) variable selection procedure, and then apply Kriging model and empirical Kriging model for quantitative structure-activity/property relationship (QSAR/QSPR) research based on the selected important variables. We demonstrate the proposed procedure retains the virtues of both variable selection and Kriging model.

Adsorption energies for a nanoporous carbon from gas–solid chromatography and molecular mechanics

Gas-solid chromatography was used to obtain second gas-solid virial coefficients, B2s, in the temperature range 342-613 K for methane, ethane, propane, butane, 2-methylpropane, chloromethane, chlorodifluoromethane, dichloromethane, and dichlorodifluoromethane. The adsorbent used was Carbosieve S-III (Supelco), a carbon powder with fairly uniform, predominately 0.55 nm slit width pores and a N2 BET surface area of 995 m2/g. The temperature dependence of B2s was used to determine experimental values of the gas-solid interaction energy, E*, for each of these molecular adsorbates. MM2 and MM3 molecular mechanics calculations were used to determine the gas-solid interaction energy, E*(cal), for each of the molecules on various flat and nanoporous model surfaces. The flat model consisted of three parallel graphene layers with each graphene layer containing 127 interconnected benzene rings. The nanoporous model consisted of two sets of three parallel graphene layers adjacent to one another but separated to represent the pore diameter. A variety of calculated adsorption energies, E*(cal), were compared and correlated to the experimental E* values. It was determined that simple molecular mechanics could be used to calculate an attraction energy parameter between an adsorbed molecule and the carbon surface. The best correlation between the E*(cal) and E* values was provided by a 0.50 nm nanoporous model using MM2 parameters.

Artificial neural network prediction of quantitative structure: Retention relationships of polycyclic aromatic hydocarbons in gas chromatography

A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature- programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al. [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (+-3 %).

Optimized Partition of Minimum Spanning Tree for Piecewise Modeling by Particle Swarm Algorithm. QSAR Studies of Antagonism of Angiotensin II Antagonists

In quantitative structure-activity relationship (QSAR) modeling, when compounds in a training set exhibit a significant structural distinction between each other, in particular when chemicals of biological interest interacting on the receptor involve a different mechanism, it might be difficult to construct a single linear model for the whole population of compounds of interest with desired residuals. Developing a piecewise linear local model can be effective to circumvent the aforementioned problem. In this paper, piecewise modeling by the particle swarm optimization (PMPSO) approach is applied to QSAR study. The minimum spanning tree is used for clustering all compounds in the training set to form a tree, and the modified discrete PSO is applied to divide the tree to find satisfactory piecewise linear models. A new objective function is formulated for searching the appropriate piecewise linear models. The proposed PMPSO algorithm was used to predict the antagonism of angiotensin II. The results demonstrated that PMPSO is useful for improvement of the performance of regression models.

New Approach by Kriging Models to Problems in QSAR

Most models in quantitative structure and activity relationship (QSAR) research, proposed by various techniques such as ordinary least squares regression, principal components regression, partial least squares regression, and multivariate adaptive regression splines, involve a linear parametric part and a random error part. The random errors in those models are assumed to be independently identical distributed. However, the independence assumption is not reasonable in many cases. Some dependence among errors should be considered just like Kriging. It has been successfully used in computer experiments for modeling. The aim of this paper is to apply Kriging models to QSAR. Our experiments show that the Kriging models can significantly improve the performances of the models obtained by many existing methods.

Prediction of gas chromatographic retention indices of a diverse set of toxicologically relevant compounds

For a set of 846 organic compounds, relevant in forensic analytical chemistry, with highly diverse chemical structures, the gas chromatographic Kovats retention indices have been quantitatively modeled by using a large set of molecular descriptors generated by software Dragon. Best and very similar performances for prediction have been obtained by a partial least squares regression (PLS) model using all considered 529 descriptors, and a multiple linear regression (MLR) model using only 15 descriptors obtained by a stepwise feature selection. The standard deviations of the prediction errors (SEP), were estimated in four experiments with differently distributed training and prediction sets. For the best models SEP is about 80 retention index units, corresponding to 2.1-7.2% of the covered retention index interval of 1110-3870. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups are well covered by the selected 15 descriptors. The developed models support the identification of substances in forensic analytical work by GC-MS in cases the retention data for candidate structures are not available.

Predicting second gas–solid virial coefficients using calculated molecular properties on various carbon surfaces

Gas-solid chromatography was used to obtain values of the second gas-solid virial coefficient, B2s, in the temperature range from 343 to 493 K for seven adsorbate gases: methane, ethane, propane, chloromethane, chlorodifluoromethane, dimethyl ether, and sulfur hexafluoride. Carboxen-1000, a 1200 m2/g carbon molecular sieve (Supelco Inc.), was used as the adsorbent. These data were combined with earlier work to make a combined data set of 36 different adsorbate gases variously interacting with from one to four different carbon surfaces. All B2s values were extrapolated to 403 K to create a set of 65 different gas-solid B2s values at a fixed temperature. The B2s value for a given gas-solid system can be converted to a chromatographic retention time at any desired flow rate and can be converted to the amount of gas adsorbed at any pressure in the low-coverage, Henry's law region. Beginning with a theoretical equation for the second gas-solid virial coefficient, various quantitative structure retention relations (QSRR) were developed and used to correlate the B2s values for different gas adsorbates with different carbon surfaces. Two calculated adsorbate molecular parameters (molar refractivity and connectivity index), when combined with two adsorbent parameters (surface area and a surface energy contribution to the gas-solid interaction), provided an effective correlation (r2 = 0.952) of the 65 different B2s values. The two surface parameters provided a simple yet useful representation of the structure and energy of the carbon surfaces and thus our correlations considered variation in both the adsorbate gas and the adsorbent solid.

Capillary gas chromatography–mass spectrometry of all 93 acyclic octenes and their identification in fluid catalytic cracked gasoline

The Kováts retention indices of all 93 acyclic octenes on polydimethylsiloxane and squalane as stationary phases as well as their mass spectra were measured. The means of gas chromatography-mass spectrometry (GC-MS) were used for confirmation of GC identification as well as for mass spectrometric deconvolution of the majority of gas chromatographic unseparated isomeric octene peaks. The distinction between corresponding E and Z acyclic octenes, that is either difficult or even impossible by means of GC-MS, was obtained on the basis of larger temperature coefficients of retention indices for Z isomeric octenes than for corresponding E isomers. The retention data expressed as homomorphy factors were correlated with the degree of branching, position of double bond, and position of alkyl group with respect to the double bond of acyclic octenes, and the structure-retention relationships were formulated. The 81 acyclic octenes were identified in FCC gasoline.