Showing papers by "Francisco Torrens published in 2006"

Non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-adjacency matrix: a novel approach for computational in silico screening and “rational” selection of new lead antibacterial agents

Abstract: A novel approach (TOMOCOMD-CARDD) to computer-aided “rational” drug design is illustrated. This approach is based on the calculation of the non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-adjacency matrix representing molecular structures. These TOMOCOMD-CARDD descriptors are introduced for the computational (virtual) screening and “rational” selection of new lead antibacterial agents using linear discrimination analysis. The two structure-based antibacterial-activity classification models, including non-stochastic and stochastic indices, classify correctly 91.61% and 90.75%, respectively, of 1525 chemicals in training sets. These models show high Matthews correlation coefficients (MCC=0.84 and 0.82). An external validation process was carried out to assess the robustness and predictive power of the model obtained. These QSAR models permit the correct classification of 91.49% and 89.31% of 505 compounds in an external test set, yielding MCCs of 0.84 and 0.79, respectively. The TOMOCOMD-CARDD approach compares satisfactorily with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, an in silico screening of 87 new chemicals reported in the anti-infective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new lead antibacterial compounds.

Bond-based global and local (bond, group and bond-type) quadratic indices and their applications to computer-aided molecular design. 1. QSPR studies of diverse sets of organic chemicals.

Abstract: The concept of atom-based quadratic indices is extended to a series of molecular descriptors (MDs) (both total and local) based on adjacency between edges. The kth edge-adjacency matrix (Ek) denotes the matrix of bond-based quadratic indices (non-stochastic) with respect to the canonical basis set. The kth “stochastic” edge-adjacency matrix, ESk, is here proposed as a new molecular representation easily calculated from Ek. Then, the kth stochastic bond-based quadratic indices are calculated using ESk as operators of quadratic transformations. The study of six representative physicochemical properties of octane isomers was used to compare the ability of both series of MDs to produce significant quantitative structure–property relationship (QSPR) models. Moreover, the general performance of the new MDs in this QSPR study has been evaluated with respect to other 2D/3D well-known sets of indices and the obtained results shown a quite satisfactory behavior of the present method. The novel bond-level MDs were also used for the description and prediction of the boiling point of 28 alkyl-alcohols and to the modeling of the specific rate constant (log k) of 34 derivatives of 2-furylethylenes. These models were statistically significant and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment. The comparison with other approaches (edge- and vertices-based connectivity indices, total and local spectral moments, and quantum chemical descriptors as well as E-state/biomolecular encounter parameters) expose a good behavior of our method in this QSPR studies. The approach described in this report appears to be a very promising structural invariant, useful for QSPR/QSAR studies, similarity/diversity analysis, and computer-aided “rational” molecular (drug) design.

Periodic Classification of Local Anaesthetics (Procaine Analogues)

Abstract: 1 Institut Universitari de Ciencia Molecular, Universitat de Valencia, Edifici d'Instituts de Paterna, P. O. Box 22085, E-46071 Valencia, Spain. http://www.uv.es/~uiqt/index.htm. Tel. +34 963 544 431, Fax +34 963 543 274 2 Departamento de Ciencias Experimentales, Facultad de Ciencias Experimentales, Universidad Catolica de Valencia San Vicente Martir, Guillem de Castro-106, E-46003 Valencia, Spain * Author to whom correspondence should be addressed. E-mail: francisco.torrens@uv.es Received: 16 December 2005 / Accepted: 26 January 2006 / Published: 31 January 2006 Abstract: Algorithms for classification are proposed based on criteria ( information entropy and its production). The feasibility of replacing a given anaesthetic by similar ones in the composition of a complex drug is studied. Some local anaesthetics currently in use are classified using characteristic chemical properties of different portions of their molecules. Many classification algorithms are based on information entropy. When applying these procedures to sets of moderate size, an excessive number of results appear compatible with data, and this number suffers a combinatorial explosion. However, after the equipartition conjecture , one has a selection criterion between different variants resulting from classification between hierarchical trees. According to this conjecture, for a given charge or duty, the best configuration of a flowsheet is the one in which the entropy production is most uniformly distributed. Information entropy and principal component analyses agree. The periodic law of anaesthetics has not the rank of the laws of physics: (1) the properties of anaesthetics are not repeated; (2) the order relationships are repeated with exceptions. The proposed statement is: The relationships that any anaesthetic p has with its neighbour p + 1 are approximately repeated for each period. Keywords: periodic property, periodic table, periodic law, classification, information entropy, equipartition conjecture, principal component analysis, cluster analysis, local anaesthetic, procaine analogue.

Effect of type, size and deformation on the polarizability of carbon nanotubes from atomic increments

Abstract: The interacting induced dipole polarization model is used for the calculation of the dipole–dipole polarizability α. The method is tested with single-wall carbon nanotubes (SWNTs) as a function of nanotube radius and elliptical deformation. The results are similar to ab initio reference calculations. For the zigzag tubes, the polarizability follows a remarkably simple law. The calculations effectively differentiate among SWNTs with increasing radial deformations. The polarizability and related properties can be modified continuously and reversibly by the external radial deformation. These results suggest a technology in which mechanical deformation can control chemical properties of the carbon nanotubes. Different effective polarizabilities are calculated for the atoms at the highest and lowest curvature sites. The calculations efficiently differentiate between the effective polarizabilities of the highest and lowest curvature sites. MOPAC-AM1 heat of formation per C atom shows that SWNT hydrocarbons (SWNTHCs) are less stable than planar acenes. SWNTHCs are stabilized and acenes are destabilized with increasing number of vertices. For SWNTs, the ratio of trivalent/divalent vertices is greater than that for the corresponding planar acenes.

Fractal dimension of active-site models of zeolite catalysts

Abstract: A method for the calculation of fractal surfaces of crystals is presented. The fractal dimension D of fragments of zeolites is calculated. Results compare well with reference calculations (GEPOL). The active site of Bronsted acid zeolites is modeled by a set of Al-OH-Si units. These units form 2-12-membered rings. Topological indices for the different active-site models are calculated. The comparison between GEPOL and SURMO2 allows calculating the active-site indices. Most cavities show no fractal character, while for the 6-8-unit rings, D lies in the range 4.0-4.3. The 6-ring shows the greatest D and is expected to be the most reactive.

Treatment of poly(styrene-co-methacrylic acid)/poly(4-vinylpyridine) blends in solution under liquid–liquid phase-separation conditions. A new method for phase-separation data attainment from viscosity measurements

Abstract: Phase diagrams are contributed for polymer mixture systems in solution. One polymer has proton-acceptor character and the other has growing proton-donor nature, which is reflected in the phase diagrams. Usually, these diagrams are obtained from size-exclusion chromatographic (SEC) measurements. A totally novel application, which is exposed in this report, is the construction of the phase diagram from the viscometric experiments of polymer mixtures. The evaluated binodal or cloud-point isotherms so built agree well with those from SEC. The results indicate an augmentation in the dimensions of donor polymer B, in the presence of acceptor polymer C, intensifying with the concentration of C, which is interpreted as an B-C association growing as the number of hydrogen bonds increases. An increment in the Huggins constant for BC, as the proportion of methacrylic acid in the donor copolymer increases, means an augmentation in the interaction for BC, indicating an extension of compatibility. Viscometric experiments evidence hydrogen bonds, intensifying as greater proportion of donor groups has polymer B, equal to that observed in the phase diagrams. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102:5039–5049, 2006

Quick Access to Potential Trichomonacidals through Bond Linear Indices-Trained Ligand-Based virtual Screening Models

Abstract: Trichomonas vaginalis (Tv) is the causative agent of the most common, nonviral, sexually transmitted disease in women and men world-wide. Since 1959 metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However resistance to MTZ in some patients and the great cost associated to the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, were able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in training (test) sets, respectively. These results validate the models for use in the ligand-based virtual screening. Also they showed large Matthews’ correlation coefficients (C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidenced the robustness of the obtained models. Later, both models were applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classified 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is a more important criterion for validating the models. In addition, these classification functions were applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds were synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Besides, out of the seven compounds that were screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) showed pronounced cytocidal activity at the concentration of 100µg/ml at 24h (48h) within the range of 98.66%-100% (99.40%-100%) while only two molecules (chemicals VA7-37 and VA7-38) showed high cytocidal activity at the concentration of 10µg/ml at 24h (48h): 98.38% (94.23%) and 97.59% (98.10%) correspondingly. The LDA-assisted QSAR models presented here could significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with trichomonacidal activity.

The Dragon Method in the Computational Identification of Novel Tyrosinase Inhibitors. Results Supported by Experimental Assays

Abstract: QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragons descriptors and linear discriminant analysis (LDA) are presented here. A dataset of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active dataset was processed by k-means cluster analysis to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model (Eq. 3) in the training set. External validation processes to assess the robustness and predictive power of the obtained model was carried out. This external prediction set had an accuracy of 99.44%. After that, the developed were used in ligand-based virtual screening of tyrosinase inhibitors from the literature and never considered in either training or predicting series. In this case, all screened chemicals were correctly classified by the LDA-based QSAR models. As a final point, these fitted models were used in the screening of new bipiperidines series as new tyrosinase inhibitors. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed a good correspondence. These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitors compounds.

Bond-Based Quadratic TOMOCOMD-CARDD Molecular Indices & Statistical Techniques for New Antitrichomonal Drug-like Compounds Discovery

Abstract: New antitrichomonal agents are needed to combat emerging metronidazoleresistant trichomoniasis and reduce the side-effects associated with currently available drugs. Toward this end, bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic total and bond-type quadratic indices for heteroatoms. The obtained LDA-based QSAR models, using non-stochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews’ correlation coefficients (C) of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidenced the robustness of the obtained models. Later, both models were applied to the virtual screening of 12 compounds already proved against Trichomonas Vaginalis (Tv). As a result, they correctly classified 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is a more important criterion for validating the models. In addition, these classification functions were also applied to a library of twenty-one chemicals in order to find new lead antitrichomonal agents. These compounds were synthesized and tested for in vitro activity against Tv. As expected, theoretical results almost coincided with experimental ones since there was obtained a correct classification for both models of 95.24% (20 out of 21) of the chemicals. Out of the twenty-one compounds that were screened, and synthesized, two molecules (chemicals G-1, UC-245), showed high to moderate cytocidal activity at the concentration of 10µg/ml, other two compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100µg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153 except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10µg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promissory results as a trichomonacidal drug-like compound. The LDA-based QSAR models presented here can be considered as a computer-assisted system that could potentially significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with antitrichomonal activity.

In silico Discovery of Novel Tyrosinase Inhibitors using Atom Based Linear Indices

Abstract: In the present report it is presented the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behaviour is showed between the theoretical and experimental results. These results provided a useful tool that can be used in the identification of new tyrosinase inhibitor compounds.