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Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields

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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.

Open3DQSAR’s logo displaying PLS coefficient isocontours

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References

  1. GRID version 22C (2004) Molecular Discovery Ltd., Oxford, England; http://www.moldiscovery.com/. Accessed 24 December 2009

  2. GOLPE 4.5 (1999) Multivariate Infometric Analysis S.r.l., Perugia, Italy; http://www.miasrl.com/golpe.htm. Accessed 24 December 2009

  3. Cramer RD III, Wold S (1988) Comparative Molecular Field Analysis (CoMFA). Appl. No. 237,491, filed Aug. 26, 1988

  4. SYBYL 7.3 (2009) Tripos International, St. Louis, MO, 63144, USA; http://www.tripos.com/. Accessed 24 December 2009

  5. Phase version 3.1 (2009) Schrödinger, LLC, New York, NY; http://www.schrodinger.com. Accessed 24 December 2009

  6. 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

  7. 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

    Article  CAS  Google Scholar 

  8. 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

    Article  CAS  Google Scholar 

  9. Gaussian 03, revision C.02 (2004) Wallingford, CT, USA; http://www.gaussian.com. Accessed 24 December 2009

  10. Jaguar version 7.6 (2009) Schrödinger, LLC, New York, NY, USA; http://www.schrodinger.com. Accessed 24 December 2009

  11. 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

    Article  CAS  Google Scholar 

  12. 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

  13. 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

    Article  CAS  Google Scholar 

  14. 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

    Article  CAS  Google Scholar 

  15. 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

    Article  CAS  Google Scholar 

  16. 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

    Article  CAS  Google Scholar 

  17. 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

    Article  CAS  Google Scholar 

  18. 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

    Article  CAS  Google Scholar 

  19. 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

    Article  CAS  Google Scholar 

  20. 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

    Article  CAS  Google Scholar 

  21. 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

    Article  CAS  Google Scholar 

  22. 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

    Article  CAS  Google Scholar 

  23. 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

    Article  CAS  Google Scholar 

  24. 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

    Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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

  27. Lekien F, Marsden J (2005) Tricubic interpolation in three dimensions. Int J Numer Methods Eng 63:455–471. doi:10.1002/nme.1296

    Article  Google Scholar 

  28. Brown B, Lovato J, Russell K (2006) DCDFLIB; http://people.sc.fsu.edu/∼burkardt/f_src/dcdflib/dcdflib.html. Accessed 24 December 2009

  29. 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

    Article  Google Scholar 

  30. Stewart DE, Leyk Z (1994) Meschach Library version 1.2b; http://www.math.uiowa.edu/∼dstewart/meschach/. Accessed 24 December 2009

  31. PyMOL (2009) DeLano Scientific LLC, Palo Alto, CA, USA; http://www.pymol.org. Accessed 24 December 2009

  32. MOE version 2009.10 (2009) Chemical Computing Group Inc, Montreal, Quebec, Canada; http://www.chemcomp.com. Accessed 24 December 2009

  33. Maestro version 9.0 (2009) Schrödinger LLC, New York, NY, USA; http://www.schrodinger.com. Accessed 24 December 2009

  34. Gnuplot version 4.2 (2009); http://www.gnuplot.info/. Accessed 24 December 2009

  35. 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

    Google Scholar 

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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.

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Authors and Affiliations

  1. Department of Drug Science, University of Torino, Via Pietro Giuria 9, 10125, Torino, Italy

    Paolo Tosco

  2. Department of Medicinal Chemistry, The Faculty of Pharmaceutical Sciences, University of Copenhagen, 2 Universitetsparken, 2100, Copenhagen, Denmark

    Thomas Balle

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  1. Paolo Tosco
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  2. Thomas Balle
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Correspondence to Paolo Tosco.

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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

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