Rivality index neighbourhood algorithm with density and distances weighted schemes for the building of robust QSAR classification models with high reliable applicability domain.

The rivality index (RI) is a normalized distance measurement between a molecule and their first nearest neighbours providing a robust prediction of the activity of a molecule based on the known activity of their nearest neighbours. Negative values of the RI describe molecules that would be correctly classified by a statistic algorithm and, vice versa, positive values of this index describe those molecules detected as outliers by the classification algorithms. In this paper, we have described a classification algorithm based on the RI and we have proposed four weighted schemes (kernels) for its calculation based on the measuring of different characteristics of the neighbourhood of molecules for each molecule of the dataset at established values of the threshold of neighbours. The results obtained have demonstrated that the proposed classification algorithm, based on the RI, generates more reliable and robust classification models than many of the more used and well-known machine learning algorithms. These results have been validated and corroborated by using 20 balanced and unbalanced benchmark datasets of different sizes and modelability. The classification models generated provide valuable information about the molecules of the dataset, the applicability domain of the models and the reliability of the predictions.

Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements.

The building of quantitative structure-activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.

QSAR classification and regression models for ß-secretase inhibitors using relative distance matrices.

The development of robust QSAR models to predict the activity of molecules of ß-secretase inhibitors is an area of interest due to the increase of Alzheimer's disease in patients in the global population. In this paper, we present a proposal based on the use of relative distance matrices as input data to the QSAR algorithms. These matrices store measurements of distances between the structural characteristics of pairs of molecules and between the molecules and a structural pattern extracted from the whole data set, thus efficiently representing a correlation between structural changes and activity. For the building of the classification and regression models support vector machine, tree complex and Gaussian process algorithms have been used; and for the validation of the models cross-validation, bootstrapping and y-randomizing techniques have been applied. The results obtained are close to 100% in accuracy and area under receiver operating characteristic values in classification, and close to 1.0 for r2 and 0.1 for root mean square error in regression in training and in external validation, proving the 'goodness' of the proposal.

Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers.

This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.

Comparison of representational spaces based on structural information in the development of QSAR models for benzylamino enaminone derivatives.

In this paper we study different representational spaces of molecule data sets based on 2D representation models for the building of QSAR models for the prediction of the activity of 37 benzylamino enaminone derivatives. Approximations based on classical similarity calculated from fingerprint representation of molecules and isomorphism obtained using sub-graph matching algorithms are compared to fragmentation-based approximations using partial least squares and genetic algorithms. The influence of the anchored position of a non-common moiety and the kind of substituents in the common core structure of the data set are analysed, demonstrating the anomalous behaviour of some molecules and therefore the difficulty in building prediction models. These problems are solved by considering approximate similarity models. These models tune the prediction equations based on the size of the substituent and the anchored position, by adjusting the contribution of each substituent in similarity measurements calculated between the molecule data sets.

Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters.

A study of the statistic characteristics of the multidetermination of several enological parameters - namely, alcoholic degree, volumic mass, total acidity, glycerol, total polyphenol index, lactic acid and total sulphur dioxide - depending on the spectroscopic zone employed, was carried out. The two techniques used were near infrared spectroscopy (NIRS) and Fourier transform mid infrared spectroscopy (FT-MIRS). The combination of these two regions (sum of their spectra) was also studied. NIRS yielded better results, but the use of both zones improved the determination of glycerol and total sulphur dioxide. The training and validation sets used for developing general equations were built with samples from different apellation d'origine, different wine types, etc. Partial least squares regression was used for multivariate calibration, using systematic cross validation in the calibration stage and external validation in the testing stage. Sample preparation was not required.

Study of spectral analytical data using fingerprints and scaled similarity measurements.

A new chemoinformatic model has been developed for enlarging the differences between spectra and applied to differentiation of wines according to the criteria grape origin and variety and ageing process. The model is based on generation of fingerprints from normalised spectra, using empirical parameters and a set of 120 samples. After generation of the fingerprints, similarity matrixes were built on the basis of the Tanimoto similarity index between the fingerprints of the samples. Calculation of the Tanimoto index was modified to adapt the index to the characteristics of the analytical measurements. Thus, scaling factors taking into account pattern fingerprints generated from a group of samples with common characteristics were used. In addition, a modified expression for calculating the Tanimoto index was employed. Principal-components analysis (PCA) and soft independent modelling of class analogy (SIMCA) were applied to the similarity matrixes. The results obtained are discussed as a function of the normalisation method employed, the empirical factor used in generation of the fingerprints, and selection of samples for building the pattern fingerprint, etc. Finally, results from differentiation of wines are compared with those obtained by applying PCA to the unprocessed spectra as stated by the proposed model.

Parallel algorithms for graph cycle extraction using the cyclical conjunction operator.

With a view to reducing the computational cost of extracting all the cycles from complex graphs, the authors have examined the viability here of parallel processing. Based on the cyclical conjunction operator, which uses an iterative process to extract every cycle from a graph, a study was performed of the factors intervening in the parallelization of this algorithm, namely the following: granularity of the parallel algorithm, requirements for synchronization points, and the spreading of the load across different processors. Tests were performed on two granularities and four different load distributions. Algorithm implementation is carried out using SGI MP and OpenMP libraries, and, in the light of the present findings, the authors propose a dynamically distributed fine-grain algorithm using that allows all the cycles in a complex graph to be found in an acceptable computational time.

Design and development of computer-aided chemical systems: virtual labs for teaching chemical experiments in undergraduate and graduate courses.

An environment for the construction of virtual chemistry experiments is presented. This environment is based on the E(V) = M + m model-Experiment (Virtual) = Materials + method-proposed and described herein, which allows the representation and subsequent building of chemistry experiments in virtual 3D worlds to any degree of complexity. The object-based nature of the environment not only allows its use on the Internet but also facilitates integration with other systems, while enabling the system to represent and organize knowledge in such a way that it is available to any teaching environment dealing with chemical laboratory experiments.

Solving incomplete inorganic chemical systems through a fuzzy knowledge frame.

A model for the completion and balancing of inconsistent inorganic reactions is presented. A series of fuzzy parameters is proposed. These parameters are considered within a knowledge frame representing inorganic reactions via a semantic/functional network and, through the calculation of possibility measures, allow the completion and solution of such reactions by considering new inorganic species to be added to the reaction. Species to be added are formulated on the basis of atoms present and/or absent in the reaction, along with valence, system features, and a series of flags that determines the cardinality of the possible solution set. Incorporation of the proposed model into an inorganic chemistry formulation system would contribute to the development of powerful computer-assisted learning systems, given the extent of information extracted from a reaction.

Prediction of the behaviour of a single flow-injection manifold.

This paper suggests a handy method of obtaining equations to predict the fundamental parameters of an FIA peak--travel time, baseline-to-baseline time, peak height and appearance time of the maximum for a simple configuration--by performing a small number of preliminary experiments in the region in which FIA system usually operates.

Application of the deford and hume method modified for quasi-reversible and irreversible processes to the chelates of Bi(III) with azomethine derivatives of 2-benzoylpyridine.

The polarographic behaviour of the bismuth complexes of the oxime, hydrazone and thiosemicarbazone of 2-benzoylpyridine has been studied by differential pulse polarography. The DeFord and Hume method, modified for use with quasi-reversible and irreversible processes, has been applied for the calculation of the formation constants of these chelates, which exhibit different degrees of reversibility in their electroreduction.