Includes bibliographical references and index.

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.

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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.

ABSTRACT: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.