title: Exploring 3D-QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists+.
abstract: Binding affinity data [Bioorg Med Chem (2004) 12:613-623] of thiazole and thiadiazole derivatives (n = 30) for the human adenosine A3 receptor subtype have been subjected to 3D-QSAR (Quantitative structure-activity relationships) analyses by molecular shape analysis (MSA) and molecular field analysis (MFA) techniques using Cerius2 Version 4.8. In the case of the MSA, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape-reference compound (based on the active conformation); (4) performing pairwise molecular superimposition using the maximum common subgroup (MCSG) method; (5) measuring molecular shape commonality using MSA descriptors; (6) determining other molecular features by calculating spatial, electronic and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by genetic function algorithm (GFA) or stepwise regression. The best 3D-QSAR equation (MSA) obtained from GFA technique shows 70.0% predicted variance (leave-one-out) and 77.7% explained variance. This equation shows the importance of Jurs descriptors (atomic charge weighted positive surface area, relative negative charge and relative positive charge surface area), partial moment of inertia, energy of the most stable conformer and the ratio of common overlap steric volume to volume of individual molecules. In the case of stepwise regression, the best relation showed 46.1% predicted variance and 72.3% explained variance. In the case of MFA, the major steps were (1) generating conformers and energy minimization; (2) matching atoms using a maximum common substructure (MCS) search and aligning molecules using the default options; (3) setting MFA preferences (rectangular grid with 2 A step size, charges by the Gasteiger algorithm, H+ and CH3 as probes); (4) creating the field; (5) analysis by the Genetic partial least squares (G/PLS) method. The equation obtained was of excellent statistical quality: 96.1% explained variance and 71.6% predicted variance. Statistically reliable 3D-QSAR models obtained from this study suggest that these techniques could be useful to design potent A3 receptor antagonists.
authors.affiliation: Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
authors.forename: Prosenjit
authors.initials: P
authors.lastname: Bhattacharya
authors.forename: J Thomas
authors.initials: JT
authors.lastname: Leonard
authors.forename: Kunal
authors.initials: K
authors.lastname: Roy
mesh_terms: D058918:Adenosine A3 Receptor Antagonists; D006801:Humans; D008958:Models, Molecular; D008968:Molecular Conformation; D021281:Quantitative Structure-Activity Relationship; D043684:Receptor, Adenosine A3 / Q000378:metabolism; D013844:Thiazoles / Q000737:chemistry* / Q000494:pharmacology*
chemical_list: D058918:Adenosine A3 Receptor Antagonists; D043684:Receptor, Adenosine A3; D013844:Thiazoles