Alexander Tropsha

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Validation is a crucial aspect of any quantitative structure-activity relationship (QSAR) modeling. This paper examines one of the most popular validation criteria, leave-one-out cross-validated R2 (LOO q2). Often, a high value of this statistical characteristic (q2 > 0.5) is considered as a proof of the high predictive ability of the model. In this paper,(More)
One of the most important characteristics of Quantitative Structure ActivityRelashionships (QSAR) models is their predictive power. The latter can bedefined as the ability of a model to predict accurately the target property(e.g., biological activity) of compounds that were not used for model development.We suggest that this goal can be achieved by rational(More)
After nearly five decades "in the making", QSAR modeling has established itself as one of the major computational molecular modeling methodologies. As any mature research discipline, QSAR modeling can be characterized by a collection of well defined protocols and procedures that enable the expert application of the method for exploring and exploiting ever(More)
It remains unclear how and why autoimmunity occurs. Here we show evidence for a previously unrecognized and possibly general mechanism of autoimmunity. This new finding was discovered serendipitously using material from patients with inflammatory vascular disease caused by antineutrophil cytoplasmic autoantibodies (ANCA) with specificity for proteinase-3(More)
Delaunay tessellation is applied for the first time in the analysis of protein structure. By representing amino acid residues in protein chains by C alpha atoms, the protein is described as a set of points in three-dimensional space. Delaunay tessellation of a protein structure generates an aggregate of space-filling irregular tetrahedra, or Delaunay(More)
Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable(More)
MOTIVATION Most scoring functions used in protein fold recognition employ two-body (pseudo) potential energies. The use of higher-order terms may improve the performance of current algorithms. METHODS Proteins are represented by the side chain centroids of amino acids. Delaunay tessellation of this representation defines all sets of nearest neighbor(More)
Computational ADME (absorption, distribution, metabolism, and excretion) models may be used early in the drug discovery process in order to flag drug candidates with potentially problematic ADME profiles. We report the development, validation, and application of quantitative structure-property relationship (QSPR) models of metabolic turnover rate for(More)
Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of(More)