Journal of Molecular Modeling

, Volume 11, Issue 6, pp 516–524 | Cite as

Exploring 3D-QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists+

  • Prosenjit Bhattacharya
  • J. Thomas Leonard
  • Kunal Roy
Original Paper


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 Å 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.

Figure Adenosine A3 binding affinity data of thiazole and thiadiazole derivatives have been subjected to 3D-QSAR study using molecular shape analysis and molecular field analysis.


QSAR MSA MFA Thiazole Thiadiazole Adenosine A3 receptor 



Quantitative structure–activity relationships


Genetic function approximation


Genetic partial least squares


Molecular shape analysis


Molecular field analysis



One of the authors (JTL) thanks the AICTE, New Delhi for a QIP fellowship. KR thanks the AICTE, New Delhi for a financial grant under the Career Award for Young Teachers scheme. The authors are thankful for the valuable suggestions of the Referee, which helped the authors to improve the manuscript substantially.


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Prosenjit Bhattacharya
    • 1
  • J. Thomas Leonard
    • 1
  • Kunal Roy
    • 1
  1. 1.Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical TechnologyJadavpur UniversityKolkataIndia

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