Simple linear QSAR models based on quantum similarity measures

A novel QSAR approach based on quantum similarity measures was developed an d tested in this paper. This approach consists of replacing the usual physi cochemical parameters employed in QSAR analysis, such as octanol-water part ition coefficient or Hammett sigma constant, by appropriate quantum chemica l descriptors. The methodological basis for this substitution is found in r ecent theoretical studies [J. Comput. Chem. 1998, 19, 1575-1583, J. Comput. -Aided Mol. Des. 1999, 13, 259-270], in which it was demonstrated that both molecular hydrophobic character and electronic substituent effect can be m odeled by appropriately chosen quantum self-similarity measures (QS-SM). Th e most important aim of this study was to prove that selected QS-SM descrip tors can be advantageously used in empirical QSAR analysis instead of class ical descriptors. For this purpose several QSAR correlations are proposed, in which empirical descriptors such as Hammett sigma constants or log P val ues are replaced by the appropriate QS-SM. These examples involve: (i) a se t of benzenesulfonamides which bind to human carbonic anhydrase, (ii) a set of benzylamines as competitive inhibitors of the enzyme trypsin, and (iii) a set of indole derivatives which are benzodiazepine receptor inverse agon ist site ligands. Simple linear QSAR models were developed in order to obta in mathematical relationships between the biological activity and the perti nent quantum chemical descriptors. The validity of the obtained QSAR models is supported by comparison of the observed and predicted values of the bio logical activity and by a statistical analysis based on a randomization tes t.