| Abstract
| - Novel atomic level AI topological indexes based on the adjacency matrix and distance matrix of a graph isused to code the structural environment of each atomic type in a molecule. These AI indexes, along withXu index, are successfully extended to compounds with heteroatoms in terms of novel vertex degree vm,which is derived from the valence connectivity δv of Kier−Hall to resolve the differentiation of heteroatomsin molecular graphs. The multiple linear regression (MLR) is used to develop the structure−property/activitymodels based on the modified Xu and AI indices. The efficiency of these indices is verified by high qualityQSPR/QSAR models obtained for several representative physical properties and biological activities of severaldata sets of alcohols with a wide range of non-hydrogen atoms. The results indicate that the physical propertiesstudied are dominated by molecular size, but other atomic types or groups have small influences dependenton the studied properties. Among all atomic types, −OH groups seem to be most important due to hydrogen-bonding interactions. On the contrary, −OH groups play a dominant role in biological activities studied,although molecular size is also an important factor. These results indicate that both Xu and AI indices areuseful model parameters for QSPR/QSAR analysis of complex compounds.
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