Abstract
Open3DQSAR is a freely available open-source program aimed at chemometric analysis of molecular interaction fields. MIFs can be imported from different sources (GRID, CoMFA/CoMSIA, quantum-mechanical electrostatic potential or electron density grids) or generated by Open3DQSAR itself. Much focus has been put on automation through the implementation of a scriptable interface, as well as on high computational performance achieved by algorithm parallelization. Flexibility and interoperability with existing molecular modeling software make Open3DQSAR a powerful tool in pharmacophore assessment and ligand-based drug design.
References
GRID version 22C (2004) Molecular Discovery Ltd., Oxford, England; http://www.moldiscovery.com/. Accessed 24 December 2009
GOLPE 4.5 (1999) Multivariate Infometric Analysis S.r.l., Perugia, Italy; http://www.miasrl.com/golpe.htm. Accessed 24 December 2009
Cramer RD III, Wold S (1988) Comparative Molecular Field Analysis (CoMFA). Appl. No. 237,491, filed Aug. 26, 1988
SYBYL 7.3 (2009) Tripos International, St. Louis, MO, 63144, USA; http://www.tripos.com/. Accessed 24 December 2009
Phase version 3.1 (2009) Schrödinger, LLC, New York, NY; http://www.schrodinger.com. Accessed 24 December 2009
Tosco P, Balle T (2009) Open3DQSAR: a new open-source pharmacophore explorer based on chemometric analysis of molecular interaction fields. Proceedings of “Model(l)ing´09”, 6-11 September 2009, Erlangen, Germany; http://www.chemie.uni-erlangen.de/modeling09/Abs_M09_Posters/Tosco.pdf. Accessed 24 December 2009
Tosco P, Ahring PK, Dyhring T, Peters D, Harpsøe K, Liljefors T, Balle T (2009) Complementary three-dimensional quantitative structure−activity relationship modeling of binding affinity and functional potency: a study on α 4 β 2 nicotinic ligands. J Med Chem 52:2311–2316. doi:10.1021/jm801060h
Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JH, Koseki S, Matsunaga N, Nguyen KA, Su S, Windus TL, Dupuis M, Montgomery JA (1993) General atomic and molecular electronic structure system. J Comput Chem 14:1347–1363. doi:10.1002/jcc.540141112
Gaussian 03, revision C.02 (2004) Wallingford, CT, USA; http://www.gaussian.com. Accessed 24 December 2009
Jaguar version 7.6 (2009) Schrödinger, LLC, New York, NY, USA; http://www.schrodinger.com. Accessed 24 December 2009
Schaftenaar G, Noordik JH (2000) Molden: a pre- and post-processing program for molecular and electronic structures. J Comput-Aided Mol Des 14:123–134. doi:10.1023/A:1008193805436
TURBOMOLE V6.0 (2009) a development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989-2007, TURBOMOLE GmbH, since 2007; http://www.turbomole.com. Accessed 24 December 2009
Wang JM, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21:1049–1074. doi:10.1002/1096-987X(200009)21:12<1049::AID-JCC3>3.0.CO;2-F
Kastenholz MA, Pastor M, Cruciani G, Haaksma EEJ, Fox T (2000) GRID/CPCA: a new computational tool to design selective ligands. J Med Chem 43:3033–3044. doi:10.1021/jm000934y
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics Intell Lab Syst 58:109–130. doi:10.1016/S0169-7439(01)00155-1
Clark RD, Fox PC (2004) Statistical variation in progressive scrambling. J Comput-Aided Mol Des 18:563–576. doi:10.1007/s10822-004-4077-z
Baroni M, Costantino G, Cruciani G, Riganelli D, Valigi R, Clementi S (1993) Generating Optimal Linear PLS Estimations (GOLPE): an advanced chemometric tool for handling 3D-QSAR problems. Quant Struct-Act Relat 12:9–20. doi:10.1002/qsar.19930120103
De Aguiar PF, Bourguignon B, Khots MS, Massart DL, Phan-Than-Luu R (1995) D-optimal designs. Chemometrics Intell Lab Syst 30:199–210. doi:10.1016/0169-7439(94)00076-X
Pastor M, Cruciani G, Clementi S (1997) Smart Region Definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure−activity relationships. J Med Chem 40:1455–1464. doi:10.1021/jm9608016
Baroni M, Clementi S, Cruciani G, Costantino G, Riganelli D (1992) Predictive ability of regression models. Part II: selection of the best predictive PLS model. J Chemometr 6:347–356. doi:10.1002/cem.1180060605
Centner V, Massart DL, de Noord OE, de Jong S, Vandeginste BM, Sterna C (1996) Elimination of uninformative variables for multivariate calibration. Anal Chem 68:3851–3858. doi:10.1021/ac960321m
Gieleciak R, Polanski J (2007) Modeling robust QSAR. 2. Iterative variable elimination schemes for CoMSA: application for modeling benzoic acid pK a values. J Chem Inf Model 47:547–556. doi:10.1021/ci600295z
Grohmann R, Schindler T (2008) Toward robust QSPR models: synergistic utilization of robust regression and variable elimination. J Comput Chem 29:847–860. doi:10.1002/jcc.20831
Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J, Du Croz J, Greenbaum A, Hammarling S, McKenney A, Sorensen D (1999) LAPACK Users’ Guide. Society for Industrial and Applied Mathematics, Philadelphia
Whaley RC, Petitet A (2005) Minimizing development and maintenance costs in supporting persistently optimized BLAS. Softw-Pract Exp 35:101–121. doi:10.1002/spe.626
Anglano C, Canonico M, Guazzone M, Botta M, Rabellino S, Arena S, Girardi G (2008). Peer-to-peer desktop grids in the real world: the ShareGrid project. Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGRID'08), Lyon (France), May 2008, IEEE Press. doi:10.1109/CCGRID.2008.23
Lekien F, Marsden J (2005) Tricubic interpolation in three dimensions. Int J Numer Methods Eng 63:455–471. doi:10.1002/nme.1296
Brown B, Lovato J, Russell K (2006) DCDFLIB; http://people.sc.fsu.edu/∼burkardt/f_src/dcdflib/dcdflib.html. Accessed 24 December 2009
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8:3–30. doi:10.1145/272991.272995
Stewart DE, Leyk Z (1994) Meschach Library version 1.2b; http://www.math.uiowa.edu/∼dstewart/meschach/. Accessed 24 December 2009
PyMOL (2009) DeLano Scientific LLC, Palo Alto, CA, USA; http://www.pymol.org. Accessed 24 December 2009
MOE version 2009.10 (2009) Chemical Computing Group Inc, Montreal, Quebec, Canada; http://www.chemcomp.com. Accessed 24 December 2009
Maestro version 9.0 (2009) Schrödinger LLC, New York, NY, USA; http://www.schrodinger.com. Accessed 24 December 2009
Gnuplot version 4.2 (2009); http://www.gnuplot.info/. Accessed 24 December 2009
Cross S, Cruciani G (2009) Molecular fields in drug discovery: getting old or reaching maturity? Drug Disc Today. doi:10.1016/j.drudis.2008.12.006
Acknowledgments
Open3DQSAR would never have seen the light of day without the invaluable pioneering work of Prof. Gabriele Cruciani and colleagues in the field of chemometrics applied to MIFs. We have referred to their detailed published methodologies [14, 17, 19, 20] to code Open3DQSAR’s implementation of the Smart Region Definition and Fractional Factorial Design algorithms, a task which would have been extremely hard in the absence of such outstanding guidance. We are also indebted to the authors of the progressive scrambling, UVE-PLS and IVE-PLS methodologies, as well as to the authors of their later extensions [16, 21-23]. We gratefully acknowledge the ShareGrid management team for the computing power provided through the ShareGrid distributed platform. Finally, P.T. thanks Prof. Alberto Gasco and Prof. Roberta Fruttero (Università degli Studi di Torino) for their warm support and encouragement throughout the development. Part of the work was carried out by P. T. at the University of Copenhagen under a visiting scientist grant from the Drug Research Academy. T.B. was supported by grants from the Carlsberg Foundation and the Lundbeck Foundation.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tosco, P., Balle, T. Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17, 201–208 (2011). https://doi.org/10.1007/s00894-010-0684-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00894-010-0684-x