Xavier Gironés

Fundamentals of Molecular Similarity

1. Prediction of Boiling Points of Organic Compounds from Molecular Descriptors by Using Backpropagation Neural Network.- 2. Some Relationships between Molecular Energy-Topology and Symmetry.- 3. Database Organization and Similarity Searching with E-State Indices.- 4. Similarity Searching in Chemical Databases Using Molecular Fields and Data Fusion.- 5. Topological Pharmacophore Description of Chemical Structures Using Mab-Force-Field-Derived Data and Corresponding Similarity Measures.- 6. Dissimilarity Measures: Introducing a Novel Methodology.- 7. Quantum-Mechanical Theory of Atoms in Molecules: A Relativistic Formulation.- 8. Topological Similarity of Molecules and the Consequences of the Holographic Electron Density Theorem, an Extension of the Hohenberg-Kohn Theorem.- 9. Quantum Chemical Reactivity: Beyond the Study of Small Molecules.- 10. Partitioning of Free Energies of Solvation into Fragment Contributions: Applications in Drug Design.- 11. Confronting Modern Valence Bond Theory with Momentum-Space Quantum Similarity and with Pair Density Analysis.- 12. Quantum Molecular Similarity: Theory and Applications to the Evaluation of Molecular Properties, Biological Activities and Toxicity.- 13. Self-Organizing Molecular Field Analysis (Somfa): A Tool for Structure-Activity Studies.- 14. Similarity Analysis of Molecular Interaction Potential Distributions. The Mipsim Software.

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.

Modeling antimalarial activity: application of Kinetic Energy Density Quantum Similarity Measures as descriptors in QSAR.

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.

Quantum Similarity Superposition Algorithm (QSSA): A Consistent Scheme for Molecular Alignment and Molecular Similarity Based on Quantum Chemistry

The use of the molecular quantum similarity overlap measure for molecular alignment is investigated. A new algorithm is presented, the quantum similarity superposition algorithm (QSSA), expressing the relative positions of two molecules in terms of mutual translation in three Cartesian directions and three Euler angles. The quantum similarity overlap is then used to optimize the mutual positions of the molecules. A comparison is made with TGSA, a topogeometrical approach, and the influence of differences on molecular clustering is discussed.

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.

A comparative study of isodensity surfaces using ab initio and ASA density functions

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.

Application of promolecular asa densities to graphical representation of density functions of macromolecular systems

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).

Using Molecular Quantum Similarity Measures as Descriptors in Quantitative Structure-Toxicity Relationships

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.

Modeling large macromolecular structures using promolecular densities

A procedure to easily construct fitted density functions is presented. This methodology, based on the promolecule approach, is able to handle large macromolecular systems, such as proteins. The usual procedure dealing with fitted densities has been improved by adding some restrictions, which allow faster calculations. As a main application example, molecular isodensity contours (MIDCOs) are constructed for two proteins, one of them composed of more than 50 000 atoms. MIDCOs, as a visual representation of the molecular density function, and thus an important descriptor of the molecular charge distribution, constitute a powerful tool in the understanding of molecular systems. MIDCOs are presented for both proteins, allowing exploration of their surfaces, as well as analysis of their shapes. Also, as a quantum mechanical calculation example, molecular quantum self-similarity measures are calculated for several proteins.