L-159061

Multiple linear regression (MLR) and artificial neural networks (ANN) have been used for structure-activity relationship analysis for a set of 113 AT1 receptor antagonists. The ANN model showed better performance with a 6-6-1 architecture than MLR. The results obtained from this study indicate that three descriptors, hydration energy (EH), n-octanol/water partition (LOGP), and energy of the lowest unoccupied molecular orbital (LUMO), play an important role on the activity of AT1 receptor antagonists with biphenyltetrazole structures. This information is pertinent to the further design of new AT1 receptor antagonists. 1 A plot of observed versus predicted PIC50 produced from the best nonlinear model using 6-6-1 architecture.

A QSAR study was carried out on nonpeptide angiotensin II antagonists which included a review of the literature on bioactivity and derivation of QSAR equations.The QSAR were divided into 4 groups according to the test system: rabbit, rat, guinea pig and human.Within each group, these are arranged according to potency (log I/C).Also listed is the CMR (calculated molar refractivity) which is similar to molar volume but contains a small element for polarizability, and Clog P values which give an assessment of the hydrophobic effects.The authors also used π as a measure of local hydrophobic binding sites.All the QSAR reported in the study were derived by the authors.The physicochem. parameters were autoloaded from their C-QSAR databases and the QSAR regression anal. was executed with a C-QSAR program.The authors derived 39 QSAR equations which provide an overview of the structure-activity relationship for a variety of compoundsTo the authors knowledge, these are the first QSAR for angiotensin antagonists.The most important conclusion reached is the lack of importance of hydrophobic interactions with the receptors.The relevance of the biphenyl moiety for hydrophobicity is discussed and a model of the pharmacophore is presented.