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Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

The 13C NMR chemical shift of sp2 carbon atoms in acyclic alkenes was estimated with multilayer feedforward artificial neural networks (ANNs) and multilinear regression (MLR), using as structural descriptors a vector made of 12 components encoding the environment of the resonating carbon atom. The neural network quantitative model provides better results than the MLR model calibrated with the same data. The predictive ability of both the ANN and MLR models was tested by the leave-20%-out (L20%O) cross-validation method, demonstrating the superior performance of the neural model. The number of neurons in the hidden layer was varied between 2 and 7, and three activation functions were tested in the neural model: the hyperbolic tangent or a bell-shaped function for the hidden layer and a linear or a hyperbolic tangent function for the output layer. All four combinations of activation functions give close results in the calibration of the ANN model, while for the prediction a linear output function performs ...

QSPR (Quantitative Structure-Property Relationship) models for the estimation of boiling points of organic compounds containing halogens, oxygen, or sulfur without hydrogen bonding were established with the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program developed by Katritzky and coworkers. The boiling points of 185 compounds containing oxygen or sulfur can be accurately computed with a MLR (Multi-Linear Regression) equation containing six theoretical descriptors and having the following statistical indices: r = 0.992 and s = 6.3 °C. For a set of 534 halogenated alkanes C 1 – C 4 the best MLR equation with five descriptors has r = 0.990 and s = 9.0 °C. For subsets of molecules corresponding to monoethers, monosulfides, F- and Cl-containing compounds or F-containing compounds, even better MLR models were obtained, showing that a greater accuracy can be obtained for the estimation of boiling points with QSPR models developed for restricted classes of compounds. The QSPR models developed with CODESSA allow accurate computation of the boiling points of organic compounds using simple constitutional, topological, electrostatic and quantum indices that can be computed with standard quantum chemistry packages.

The expensive and time-consuming process of drug lead discovery is significantly accelerated by efficiently screening molecular libraries with a high structural diversity and selecting subsets of m...

The reaction of 1,2-diphenylethanedione (benzil) with cyclopentadiene in the presence of sodium methoxide unexpectedly afforded the novel fully conjugated 2,3,7-triphenyl derivative of cyclopenta[c]pyran, a pyranic pseudoazulene. The structure was proved by physical methods (IR, UV/VIS; 'H-NMR, 1 3 C-NMR; MS), and confirmed by X-ray crystallography. Protonation affords a pyrylium salt with a structure that agrees with the results of semiempirical and ab-initio methods, which also agree fairly well with experimentally-determined bond lengths.