Saturated hydrocarbons are important constituents of petroleum products. Their behavior in water, the most prevalent environmental solvent, is of relevance with regard to environmental partitioning. Due to their negligible attractive interactions with water, they are suitable compounds for a mechanism-based validation of the relationship between molecular size and the solubility in water. To that end, we measured the aqueous solubility of aliphatic and alicyclic hydrocarbons with 10 to 19 carbon atoms employing the slow-stirring experiment. Moreover, we compiled data on molecular weight and molar volume at the boiling point as macroscopic size parameters and calculated quantum-chemical molecular size parameters. The aqueous solubility data span a range from 6 × 10 -6 M to 4 × 10 -11 M with coefficients of variation of less than 15% except for 2,6,10,14-tetramethylpentadecane (39%). The relationships of the experimentally determined solubility values with the macroscopic reflected the general trend of decreasing solubility with increasing molecular size, but discriminated between n-and branched alkanes. This indicates that these parameters do not reflect the solute-solvent interactions at the microscopic level. Interpretation of the experimentally observed solubility data based on theoretical considerations of the conformation and the constitution of alkanes is consistent with the following overall picture: For a given n-alkane in aqueous solution, the all-trans conformation is preferred over folded geometries. Within alkanes, molecular size is the primary determinant of their solubility in water, and increasing molecular size results in a decrease in water solubility mainly due to the increased free energy penalty for cavity formation in water. The solvent-accessible molecular volume and surface area appeared to be valid reflections of the molecular size.

The impact of orthogonal signal correction (OSC) on the prediction power of CoMFA models was studied using a data set of 47 nitrobenzenes with toxicities (log 1/IC 50 ) towards the aquatic ciliates Tetrahymena pyriformis. Comparative analyses of different data pre-treatments shows that block unscaled weighting (BUW) results in significantly better PLS models than no scaling, centering or autoscaling for OSC. One OSC component is optimal for the signal correction and reduces the X variance by about 40%. While OSC yields improved calibration and cross-validation statistics, standard CoMFA is superior with respect to the external prediction power as evaluated by models built from complementary subsets. Moreover, external prediction reveals some cases of severe OSC overfitting, which needs attention in future investigations.Quant. Struct.-Act. Relat., 21 (

Crystal structures taken from the Cambridge Structural Database were used to build a ring scaffold database containing 19 050 3D structures, with each such scaffold then being used to generate a centroid connecting path (CCP) representation. The CCP is a novel object that connects ring centroids, ring linker atoms, and other important points on the connection path between ring centroids. Unsupervised searching in the scaffold and CCP data sets was carried out using the atom-based LAMDA and RigFit search methods and the field-based similarity search method. The performance of these methods was tested with three different ring scaffold queries. These searches demonstrated that unsupervised 3D scaffold searching methods can find not only the types of ring systems that might be retrieved in carefully defined pharmacophore searches (supervised approach) but also additional, structurally diverse ring systems that could form the starting point for lead discovery programs or other scaffold-hopping applications. Not only are the methods effective but some are sufficiently rapid to permit scaffold searching in large chemical databases on a routine basis.

The flow patterns of 20 organic liquids with diverse structures and functionalities between electrodes were measured under a dc electric field. The results clearly showed the existence of a strong relationship between the flow pattern of a compound and its molecular structure. On the basis of a variety of 23 molecular descriptors including those obtained by quantum-chemical calculations, multiple regression analysis and discriminant analysis were applied to identify the significant factors contributing to the flow patterns. For the flow rate dipole moment, nucleophilic delocalizability and lipophilicity as expressed by the 1-octanol/water partition coefficient were found to be the key factors as judged by a five-value regression model with a squared correlation coefficient (r2) of 0.881. For the direction of the flow, just two quantum-chemical parameters, namely, absolute hardness and the self-polarizability normalized by molecular volume, were identified as significant factors by using linear discriminant analysis. The numbers of misclassified compounds were only one and two for training and prediction (leave-one-out cross-validation), respectively, by the best discriminant model.