Biomedical subjects
Publications and source records attributed to R Carbó-Dorca.
Regions of space are defined to maximize the probability to find a given number of electrons within. Their chemical significance and their relationship to the electron localization function (ELF) are explored by analyzing the results for a few linear molecules: LiH, BH, N2, CO, CS, C2H2, and C4H2.
In this article we report the application of the Promolecular Atomic Shell Approximation (Promolecular ASA) to the graphical representation of the density function (DF) of large macromolecular systems. Promolecular ASA DF, constructed from previously computed and fitted atomic densities, provides a fast and practical representation of Molecular IsoDensity Contours (MIDCOs). These representations can be extended to macromolecular systems composed by > 1000 atoms easily and with low computational costs, allowing the visualization of protein DF. The method is at first presented with a small molecule (2,4,6-trinitrophenol), comparing the resulting ASA MIDCOs with direct ab initio contours. For macromolecular tests the Promolecular ASA densities are also applied to the generation of macromolecular density surfaces of two proteins: myoglobin (2541 atoms) and gene V protein (1362 atoms).
The establishment of quantitative structure-activity relationship (QSAR) models for the toxicity of polycylic aromatic hydrocarbons (PAHs) is described. Two properties, in vitro percutaneous absorption in rat skin and discrete levels of carcinogenic activity, are examined using molecular quantum similarity measures (MQSM). The results show that MQSM produces comparable, or even better, results than other approaches using physicochemical, topological and quantum-chemical molecular descriptors. Furthermore, a careful analysis puts into evidence that most of the information characterized by the original descriptors is in fact contained in the molecular density functions, the basis of MQSM. The present paper, together with several other reported by our laboratory, proves that MQSM might be appropriate theoretical tools for QSAR and computer-aided drug design, comparable to other highly predictive QSAR methodologies.
In this work, the antimalarial activity of two series of 20 and 7 synthetic 1,2,4-trioxanes and a set of 20 cyclic peroxy ketals are tested for correlation search by means of Molecular Quantum Similarity Measures (MQSM). QSAR models, dealing with different biological responses (IC90, IC50 and ED90) of the parasite Plasmodium Falciparum, are constructed using MQSM as molecular descriptors and are satisfactorily correlated. The statistical results of the 20 1,2,4-trioxanes are deeply analyzed to elucidate the relevant structural features in the biological activity, revealing the importance of phenyl substitutions.
Electron-electron repulsion energy ( ) is presented as a new molecular descriptor to be employed in QSAR and QSPR studies. Here it is shown that this electronic energy parameter is connected to molecular quantum similarity measures (MQSM), and as a consequence can be considered as a complement to steric and electronic parameters in description of molecular properties and biological responses of organic compounds. The present strategy considers the molecule as a whole, thus there is no need to employ contributions of isolated fragments as in many calculations of molecular descriptors, like log P or the Free-Wilson analysis. The procedure has been tested in a widespread set of molecules: alcohols, alkanamides, indole derivatives and 1-alkylimidazoles. Molecular properties, as well as toxicity, are correlated using as a parameter, and extensions to the method are given for handling difficult systems. In almost all studied cases, satisfactory linear relationships were finally obtained.
A novel QSAR approach based on quantum similarity measures was developed and tested in this paper. This approach consists of replacing the usual physicochemical parameters employed in QSAR analysis, such as octanol-water partition coefficient or Hammett sigma constant, by appropriate quantum chemical descriptors. The methodological basis for this substitution is found in recent theoretical studies [J. Comput. Chem. 1998, 19, 1575-1583, J. Comput. -Aided Mol. Des. 1999, 13, 259-270], in which it was demonstrated that both molecular hydrophobic character and electronic substituent effect can be modeled by appropriately chosen quantum self-similarity measures (QS-SM). The most important aim of this study was to prove that selected QS-SM descriptors can be advantageously used in empirical QSAR analysis instead of classical descriptors. For this purpose several QSAR correlations are proposed, in which empirical descriptors such as Hammett sigma constants or log P values are replaced by the appropriate QS-SM. These examples involve: (i) a set of benzenesulfonamides which bind to human carbonic anhydrase, (ii) a set of benzylamines as competitive inhibitors of the enzyme trypsin, and (iii) a set of indole derivatives which are benzodiazepine receptor inverse agonist site ligands. Simple linear QSAR models were developed in order to obtain mathematical relationships between the biological activity and the pertinent quantum chemical descriptors. The validity of the obtained QSAR models is supported by comparison of the observed and predicted values of the biological activity and by a statistical analysis based on a randomization test.
The objective of this work is to demonstrate that an appropriate treatment of quantum similarity matrices can reveal hidden data grouping related to relevant structural features and even to biological properties of interest. Classical scaling is used here to extract the information contained in the similarity relationships between the elements of a molecular set. Facet theory is invoked to relate, in a qualitative way, the spatial regions to structural characteristics as well as to properties of interest. Two application examples are discussed: the Cramer steroid set and a benzene, toluene and xylene derivatives set.
Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic molecular properties have been used as molecular descriptors to determine such relationships. Among all these parameters, Hammett sigma constants and the logarithm of the octanol-water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new molecular descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a molecular descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent molecular descriptors usable in general QSPR applications of chemical interest.
In this paper molecular quantum similarity measures (MQSM) are used to describe molecular toxicity and to construct Quantitative Structure-Toxicity Relationships (QSTR) models. This study continues the recently described relationships between MQSM and log P values, which permits to use the theoretical MQSM as an alternative to the empirical hydrophobic parameter in QSPR studies. In addition a new type of MQSM is presented in this work: it is based on the expectation value of electron-electron repulsion energy. The molecular properties studied here, as application examples are aquatic toxicity, toxicology on Bacteria and inhibition of a macromolecule employing four different molecular sets.
In this article, we report a visual comparison between several of the available methods for constructing electronic density functions. The density forms studied include ab initio, atomic shell approximation, and promolecular densities. A graphical comparison is made for six different molecules at different levels of density function values. The differences between the various density functions are analysed by considering a molecular quantum self-similarity measure and the required computational time for all molecules at all computation levels is considered.
A novel approach to construct theoretical QSAR models is proposed. This technique, based on the systematic use of quantum similarity measures as theoretical molecular descriptors, opens the possibility to localize and to identify the position of the bioactive part of drug molecules in situations, where the nature of the pharmacophore is not known. To test the reliability of this new approach, the method has been applied to the study of steroids binding to corticosteroid-binding human globulin. The studied molecules involved the set of 31 Cramer's steroids, often used as a benchmark set in QSAR studies. It has been shown that theoretical QSAR models based on the present procedure are superior to those derived from alternative existing approaches. In addition, a new method to measure the statistical significance of multiparameter QSAR models is also proposed.
In this work, is studied the application, within a quantum similarity framework, of the recently described Kinetic Energy Density Function in the evaluation of the antimalarial activity. First, this new type of Density Function is briefly presented from its theoretical foundations, and its inclusion in the molecular quantum similarity is discussed afterward. The application of Kinetic Energy-based Quantum Similarity Measures to QSAR is tested with 2 molecular sets composed of artemisinin derivatives, in which the 50% inhibition of synthesis and reduction of hidrofolate (IC50) in different Plasmodium falciparum clones are analyzed. Satisfactory correlations are obtained for all antimalarial activities in all studied molecular sets. Molecular Quantum Similarity analysis provides a consistent, unbiased, and homogeneous set of molecular descriptors and is a feasible alternative to the use of classical physicochemical descriptors.
This study presents an alternative of the Electronegativity Equalization Method (EEM), where the usual Coulomb kernel has been transformed into a smooth function. The new framework, as the classical EEM, permits fast calculations of atomic charges in a given molecule for a small computational cost. The original EEM procedure needs to previously calibrate the different implied atomic hardness and electronegativity, using a chosen set of molecules. In the new EEM algorithm half the number of parameters needs to be calibrated, since a relationship between electronegativities and hardnesses has been found.
In this work, a new methodology to construct a tuned QSAR model is presented, which is based on a convex set formalism. The present procedure continues previous 3D QSAR studies, performed using molecular quantum similarity measures (MQSM). With this new computational tool, the efficiency of MQSM applied to QSAR analysis is significantly improved. A reliable QSAR model is obtained using convex linear combinations of different kinds of MQSM, corresponding to different quantum-mechanical operators related to the quantum similarity integral. The active compounds studied here, as a case study, are a set of antitumor agents, the camptothecin molecule and analogues, and the property evaluated is the topoisomerase-I inhibition activity. Before performing a tuned QSAR analysis with this particular molecular set, a simple QSAR study for all the different possible types of MQSM is carried out. In addition, another application of MQSM is presented, to determine which method can be used to optimize molecular structures in order to reproduce experimental molecular geometries as well as possible.
Predictive models based on tuned molecular quantum similarity measures and their application to obtain quantitative structure-activity relationships (QSAR) are described. In the present paper, the corticosteroid-binding globulin binding affinity of a 31 steroid family is studied by means of a multilinear regression using molecular descriptors derived from mixed steric-electrostatic quantum similarity matrixes as parameters, obtaining satisfactory predictions. A systematic procedure to treat outliers by using triple-density quantum similarity measures is also presented. This method depicts an alternative to the grid-based QSAR techniques, providing a consistent approach that avoids problematic result dependency on the grid parameters.
Controlled modifications in certain protein amino acid residues can lead to changes in their function and stability. Amino acid structural features and their relation to these changes were examined by using quantum molecular similarity techniques. The effect of deliberate mutations in position 172 of the haloalkane dehalogenase enzyme, yielding to variations on the dehalogenation of 1,2-dibromoethane, was studied qualitatively and quantitatively using molecular quantum similarity techniques. A valuable classification of the residues according to their effect on activity was obtained by representing the optimal two-dimensional classical scaling solution. In addition, satisfactory quantitative relationships were found, comparable to those attained by previous studies on this same data set using other techniques. Molecular quantum similarity analysis provides a consistent, unbiased, and homogeneous set of molecular descriptors and is a feasible alternative to the use of physicochemical properties.