QSAR modeling based on structure-information for properties of interest in human health

Abstract

The development of QSAR models based on topological structure description is presented for problems in human health. These models are based on the structure-information approach to quantitative biological modeling and prediction, in contrast to the mechanism-based approach. The structure-information approach is outlined, starting with basic structure information developed from the chemical graph (connection table). Information explicit in the connection table (element identity and skeletal connections) leads to significant (implicit) structure information that is useful for establishing sound models of a wide range of properties of interest in drug design. Valence state definition leads to relationships for valence state electronegativity and atom/group molar volume. Based on these important aspects of molecules, together with skeletal branching patterns, both the electrotopological state (E-state) and molecular connectivity (chi indices) structure descriptors are developed and described. A summary of four QSAR models indicates the wide range of applicability of these structure descriptors and the predictive quality of QSAR models based on them: aqueous solubility (5535 chemically diverse compounds, 938 in external validation), percent oral absorption (%OA, 417 therapeutic drugs, 195 drugs in external validation testing), AMES mutagenicity (2963 compounds including 290 therapeutic drugs, 400 in external validation), fish toxicity (92 substituted phenols, anilines and substituted aromatics). These models are established independent of explicit three-dimensional (3-D) structure information and are directly interpretable in terms of the implicit structure information useful to the drug design process.