3D-QSAR approach has been widely applied and proven to be useful in the case where no reliable crystal structure of the complex between a biologically active molecule and the receptor is available. At the same time, however, it also has highlighted the sensitivity of this approach. The main requirement of the traditional 3D-QSAR method is that molecules should be correctly overlaid in what is assumed to be the bioactive conformation. Identifying an active conformation of a flexible molecule is technically difficult. It has been a bottleneck in the application of the 3D-QSAR method. We have developed a 3D-QSAR software named AutoGPA especially based on an automatic pharmacophore alignment method in order to overcome this problem which has discouraged general medicinal chemists from applying the 3D-QSAR methods to their “real-world” problems. Applications of AutoGPA to three inhibitor-receptor systems have demonstrated that without any prior information about the three-dimensional structure of the bioactive conformations AutoGPA can automatically generate reliable 3D-QSAR models. In this paper, the concept of AutoGPA and the application results will be described. 1. Introduction There are two major types of in silico drug discovery techniques: structure-based and ligand-based techniques. Quantitative structure-activity relationship (QSAR) approach only based on biological activities and chemical structures of a series of molecules with the modest biological activities is one of the ligand-based techniques. The QSAR approach explicitly considering three-dimensional shape of molecules is called 3D-QSAR. The CoMFA method proposed by Cramer et al. [1] is one of the 3D-QSAR approaches which has been widely applied and proven that the 3D-QSAR approach is better than the traditional QSAR one. The CoMFA method is based on the idea that biological activity can be analyzed by relating the shape-dependent steric and electrostatic field of molecules to their biological activity. The results of a 3D-QSAR depend on a number of factors, each of which must be carefully considered. One of the most important considerations involves the selection of biologically active conformations and their alignment prior to the analysis. This may be relatively straightforward when one is working with a congeneric series of compounds that all have some key structural features that can be overlaid. For example, the original CoMFA paper [1] examined a series of steroid molecules which can be overlaid easily using the rigid steroid nucleus. In most cases, however, molecules of interest for
References
[1]
R. D. Cramer, D. E. Patterson, and J. D. Bunce, “Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins,” Journal of the American Chemical Society, vol. 110, no. 18, pp. 5959–5967, 1988.
[2]
“Molecular Operating Environment (MOE),” Chemical Computing Group Inc., 2010.
[3]
T. A. Halgren, “MMFF VI. MMFF94s option for energy minimization studies,” Journal of Computational Chemistry, vol. 20, no. 7, pp. 720–729, 1999.
[4]
M. Wojciechowski and B. Lesyng, “Generalized Born model: analysis, refinement, and applications to proteins,” Journal of Physical Chemistry B, vol. 108, no. 47, pp. 18368–18376, 2004.
[5]
P. Labute, C. Williams, M. Feher, E. Sourial, and J. M. Schmidt, “Flexible alignment of small molecules,” Journal of Medicinal Chemistry, vol. 44, no. 10, pp. 1483–1490, 2001.
[6]
I. Islam, J. Bryant, Y.-L. Chou et al., “Indolinone based phosphoinositide-dependent kinase-1 (PDK1) inhibitors. Part 1: design, synthesis and biological activity,” Bioorganic and Medicinal Chemistry Letters, vol. 17, no. 14, pp. 3814–3818, 2007.
[7]
I. Islam, G. Brown, J. Bryant et al., “Indolinone based phosphoinositide-dependent kinase-1 (PDK1) inhibitors. Part 2: optimization of BX-517,” Bioorganic and Medicinal Chemistry Letters, vol. 17, no. 14, pp. 3819–3825, 2007.
[8]
M. D. M. Abdul-Hameed, A. Hamza, J. Liu, and C. G. Zhan, “Combined 3D-QSAR modeling and molecular docking study on indolinone derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1,” Journal of Chemical Information and Modeling, vol. 48, no. 9, pp. 1760–1772, 2008.
[9]
F. A Pasha, M. Muddassar, A. K. Srivastava, and S. J. Cho, “In silico QSAR studies of anilinoquinolines as EGFR inhibitors,” Journal of Molecular Modeling, vol. 16, no. 2, pp. 263–277, 2010.
[10]
S. L. Dixon, A. M. Smondyrev, E. H. Knoll, S. N. Rao, D. E. Shaw, and R. A. Friesner, “PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results,” Journal of Computer-Aided Molecular Design, vol. 20, no. 10-11, pp. 647–671, 2006.
[11]
F. Manetti, S. Schenone, F. Bondavalli et al., “Synthesis and 3D QSAR of new pyrazolo[3,4-b]pyridines: potent and selective inhibitors of A1 adenosine receptors,” Journal of Medicinal Chemistry, vol. 48, no. 23, pp. 7172–7185, 2005.
[12]
A. Tafi, C. Bernardini, M. Botta et al., “Pharmacophore based receptor modeling: the case of adenosine A3 receptor antagonists. An approach to the optimization of protein models,” Journal of Medicinal Chemistry, vol. 49, no. 14, pp. 4085–4097, 2006.
