David Robert

Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: prediction of the corticosteroid-binding globulin binding affinity for a steroid family.

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.

TGSA: A molecular superposition program based on topo-geometrical considerations

In this article, a new molecular alignment procedure to provide general‐purpose, fast, automatic, and user‐intuitive three‐dimensional molecular alignments is presented. This procedure, called Topo‐Geometrical Superposition Approach (TGSA), is only based on comparisons of atom types and interatomic distances; hence, the procedure can handle large molecular sets within affordable computational costs. The method is able to accurately align 3D structures using the common molecular substructures, as inferred by the bonding pattern (atom correspondences), where present. The algorithm has been implemented into a program named TGSA99, and it has been tested over eight different molecular sets: flavilium salts, amino acids, indole derivatives, AZT, steroids, anilide derivatives, poly‐aromatic‐hydrocarbons, and inhibitors of thrombine. The TGSA algorithm performance is evaluated by means of computational time, number of superposed atoms, and index of fit between the compared structures.

MOLECULAR QUANTUM SIMILARITY MEASURES TUNED 3D QSAR : AN ANTITUMORAL FAMILY VALIDATION STUDY

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.

A Formal Comparison between Molecular Quantum Similarity Measures and Indices

In this paper two methods are described to establish a formal comparison between different molecular quantum similarity measures (MQSM) and indices (MQSI) to detect redundancies in the information produced. The methods used are the Procrustes analysis and a proximity measure involving standarized similarity vectors. A small set of molecules, the fluoro- and chloro-substituted methanes, are used as an illustrative example, and conclusions obtained by direct comparison of the similarity matrixes are retrieved.

Use of electron-electron repulsion energy as a molecular descriptor in QSAR and QSPR studies

Electron-electron repulsion energy (〈 Vee〉) 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 〈 Vee〉 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.

Quantum Molecular Similarity: Theory and Applications to the Evaluation of Molecular Properties, Biological Activities and Toxicity

In this chapter we present an updated revision of the mathematical interpretation leading to further development of the theory and practice associated with quantum similarity measures (QSM) [1–40]. The role of QSM can be resumed on their ability to be the vehicle producing discrete ndimensional mathematical representations of molecular structures. This property transforms QSM into a general source of unbiased molecular descriptors. QSM descriptors are general indeed, because of their quantum chemical origin: They can be computed, in principle, for any quantum system or any molecular structure possessing arbitrary geometrical conformation or state. Moreover, QSM descriptors are unbiased, because their values are not chosen by a priori designs: They are built up as a consequence of the theoretical quantum framework results and only depend on the nature and topological characteristics of the studied molecular set.

Aromatic Compounds Aquatic Toxicity QSAR Using Molecular Quantum Similarity Measures

Abstract Molecular Quantum Similarity is discussed to be a satisfactory chemical tool to develop QSARs for the toxicity of aromatic narcotic pollutants to fish Poecilia reticulata. The method is presented as a possible alternative to the use of classical 2D QSAR descriptors, such as logP, Hammetts sigma, or HOMO and LUMO energies, and it is based upon the information extracted from the quantitative similarity measures between the overall molecular pairs of the studied set. A systematic study of medium size homogeneous sets is performed, and finally an analysis of the full set is carried out. In all cases, satisfactory prediction models are achieved using few parameters. The stability and reliability of QSARs are tested by cross-validation, randomisation and real prediction.

General trends in atomic and nuclear quantum similarity measures

Simple and accurate relationships between atomic and nuclear quantum similarity measures and their constituent elements were found. These results complement findings in previous studies in which quantum self-similarity measures in atoms and nuclei were linked to the atomic and mass numbers, respectively. The models were validated on a large test set, and the general trends in the behavior of the quantum similarity measures for these quantum objects were made clear.

Facet diagrams for quantum similarity data

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 steriod set and a benzene, toluene and xylene derivatives set.

Quantification of the influence of single-point mutations on haloalkane dehalogenase activity: a molecular quantum similarity study.

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.