Bucumolol

From the deductive point of view, neurotransmitter receptors can be divided into categories such as cholinergic (muscarinic, nicotinic), adrenergic (alpha- and beta-), dopaminergic, serotoninergic (5-HT1 approximately 5-HT5), and histaminergic (H1 and H2). Selective agonists and antagonists of each receptor subtype can have specific useful therapeutic applications. For understanding the molecular mechanisms of action, an inductive method of analysis is useful.

The aim of the present study is to examine the structure-activity relationships of agents acting on G-protein coupled receptors.

Representative sets of G-PCR agonists and antagonists were identified from the literature and Medline [P.M. Walsh (2003) Physicians' Desk Reference; M.J. O'Neil (2001) The Merck Index]. The molecular weight (MW), calculated logarithm of octanol/water partition coefficient (C log P) and molar refraction (CMR), dipole moment (DM), E(lumo) (the energy of the lowest unoccupied molecular orbital, a measure of the electron affinity of a molecule and its reactivity as an electrophile), E(homo) (the energy of the highest occupied molecular orbital, related to the ionization potential of a molecule, and its reactivity as a nucleophile), and the total number of hydrogen bonds (H(b)) (donors and receptors), were chosen as molecular descriptors for SAR analyses.

The data suggest that not only do neurotransmitters share common structural features but their receptors belong to the same ensemble of G-protein coupled receptor with seven to eight transmembrane domains with their resultant dipoles in an antiparallel configuration. Moreover, the analysis indicates that the receptor exists in a dynamic equilibrium between the closed state and the open state. The energy needed to open the closed state is provided by the hydrolysis of GTP. A composite 3-D parameter frame setting of all the neurotransmitter agonists and antagonists are presented using MW, Hb and mu as independent variables.

It appears that all neurotransmitters examined in this study operate by a similar mechanism with the G-protein coupled receptors.

The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter- and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.