Spectral moments of phenylenes

In a series of publications Estrada (Estrada, E. J. Chem. Inf. Comput. Sci. 1996, 36, 844-849; 1997 37, 320-328; 1998, 38, 23-27) employed spectral moments of line graphs in QSPR and QSAR relationship studies of various classes of compounds. A recent paper (Markovi?, S.; Gutman, I. J. Chem. Inf Comput. Sci. 1999, 39, 289-293) reported that in QSPR and QSAR investigations of benzenoid hydrocarbons based on linear combination of spectral moments, it made no difference whether one used spectral moments of the molecular graph or those of the line graph. In the present work spectral moments of molecular graphs (Mk) and line graphs (muk) of phenylenes are considered. The first few Mk's and muk'S of phenylenes are dependent on identical structural parameters. It is proved that the two sets of moments of phenylenes are linearly dependent. It is also shown that in the case of the heat of formation of phenylenes there is no advantage in using lower spectral moments of line graphs instead of lower spectral moments of molecular graphs. In this way the redundancy observed in the case of benzenoid hydrocarbons is also shown to exist in the class of phenylenes.     

Rapid evaluation of synthetic and molecular complexity for in silico chemistry

Methods that rapidly evaluate molecular complexity and synthetic feasibility are becoming increasingly important for in silico chemistry. We propose a new metric based on relative atomic electronegativities and bond parameters that evaluate both synthetic and molecular complexity (SMCM) starting from chemical structures. Against molecular weight, SMCM has the lowest fraction of adjusted variance (R2=0.535) on a series of 261,048 diverse compounds, when compared to the complexity metric of Baron and Chanon (R2=0.777; J. Chem. Inf. Comput. Sci. 2001, 41, 269-272) and Rücker (R2=0.895 for log complexity values; J. Chem. Inf. Comput. Sci. 2004, 44, 378-386), respectively. These metrics are in general agreement when the metabolic synthesis of cholesterol from S-3-hydroxy-3-methyl-glutaryl coenzyme A is monitored, indicating that SMCM can be useful in discerning increases in complexity. Because the presence of substructure patterns can be directly incorporated into this scheme, SMCM is relatively straightforward and can be easily tailored to rapidly evaluate virtual (combinatorial) libraries and high throughput screening hits.     

GA strategy for variable selection in QSAR studies: application of GA-based region selection to a 3D-QSAR study of acetylcholinesterase inhibitors

Comparative molecular field analysis (CoMFA) with partial least squares (PLS) is one of the most frequently used tools in three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. Although many successful CoMFA applications have proved the value of this approach, there are some problems in its proper application. Especially, the inability of PLS to handle the low signal-to-noise ratio (sample-to-variable ratio) has attracted much attention from QSAR researchers as an exciting research target, and several variable selection methods have been proposed. More recently, we have developed a novel variable selection method for CoMFA modeling (GARGS: genetic algorithm-based region selection), and its utility has been demonstrated in the previous paper (Kimura, T., et al. J. Chem. Inf. Comput. Sci. 1998, 38, 276-282). The purpose of this study is to evaluate whether GARGS can pinpoint known molecular interactions in 3D space. We have used a published set of acetylcholinesterase (AChE) inhibitors as a test example. By applying GARGS to a data set of AChE inhibitors, several improved models with high internal prediction and low number of field variables were obtained. External validation was performed to select a final model among them. The coefficient contour maps of the final GARGS model were compared with the properties of the active site in AChE and the consistency between them was evaluated.