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One of the most important characteristics of Quantitative Structure Activity Relashionships (QSAR) models is their predictive power. The latter can be defined 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(More)
Mutational experiments show how changes in the hydrophobic cores of proteins affect their stabilities. Here, we estimate these effects computationally, using four-body likelihood potentials obtained by simplicial neighborhood analysis of protein packing (SNAPP). In this procedure, the volume of a known protein structure is tiled with tetrahedra having the(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)
A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC(50) using chemical topological descriptors(More)
Topological descriptors of chemical structures (such as molecular connectivity indices) are widely used in Quantitative Structure-Activity Relationships (QSAR) studies. Unfortunately, these descriptors lack the ability to discriminate between stereoisomers, which limits their application in QSAR. To circumvent this problem, we recently introduced chirality(More)
Three-dimensional structure and amino acid sequence of proteins are related by an unknown set of rules that is often referred to as the folding code. This code is believed to be significantly influenced by nonlocal interactions between the residues. A quantitative description of nonlocal contacts requires the identification of neighboring residues. We(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)
Finding recurring residue packing patterns, or spatial motifs, that characterize protein structural families is an important problem in bioinformatics. We apply a novel frequent subgraph mining algorithm to three graph representations of protein three-dimensional (3D) structure. In each protein graph, a vertex represents an amino acid. Vertex-residues are(More)
A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most(More)