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ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics

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Abstract

We present ElectroShape, a novel ligand-based virtual screening method, that combines shape and electrostatic information into a single, unified framework. Building on the ultra-fast shape recognition (USR) approach for fast non-superpositional shape-based virtual screening, it extends the method by representing partial charge information as a fourth dimension. It also incorporates the chiral shape recognition (CSR) method, which distinguishes enantiomers. It has been validated using release 2 of the Directory of useful decoys (DUD), and shows a near doubling in enrichment ratio at 1% over USR and CSR, and improvements as measured by Receiver Operating Characteristic curves. These improvements persisted even after taking into account the chemotype redundancy in the sets of active ligands in DUD. During the course of its development, ElectroShape revealed a difference in the charge allocation of the DUD ligand and decoy sets, leading to several new versions of DUD being generated as a result. ElectroShape provides a significant addition to the family of ultra-fast ligand-based virtual screening methods, and its higher-dimensional shape recognition approach has great potential for extension and generalisation.

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References

  1. Leach AR, Gillet VJ, Lewis RA, Taylor R (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem ASAP

  2. Köppen H (2009) Virtual screening: what does it give us?. Curr Opin Drug Discov Dev 12(3):397–407

    Google Scholar 

  3. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865

    Article  CAS  Google Scholar 

  4. Kubinyi H (2006) Pharmaceutical research and development

  5. NCI Diversity Set II, http://www.dtp.nci.nih.gov/branches/dscb/div2_explanation.html

  6. Nicholls A, McGaughey GB, Sheridan RP, Good AC, Warren G, Mathieu M, Muchmore SW, Brown SP, Grant JA, Haigh JA, Nevins N, Jain AN, Kelley B (2010) Molecular shape and medicinal chemistry: a perpective. J Med Chem. doi:10.1021/jm900818s

  7. Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996

    CAS  Google Scholar 

  8. Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trend Pharmacol Sci 30:138–147

    Article  CAS  Google Scholar 

  9. Patterson DE, Cramer RD, Ferguson AM, Clark RD, Weinberger LE (1996) Neighborhood behavior: a useful concept for validation of molecular diversity descriptors. J Med Chem 39:3049–3059

    Article  CAS  Google Scholar 

  10. Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82

    Article  CAS  Google Scholar 

  11. Grant JA, Gallardo MA, Pickup B (1996) A fast method of molecular shape comparison: a simple application of a gaussian description of molecular shape. J Comp Chem 17(14):1653–1666

    Article  CAS  Google Scholar 

  12. Rush TS, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J Med Chem 48(5):1489–1495

    Article  CAS  Google Scholar 

  13. Sheridan RP, McGaughey GB, Cornell WD (2008) Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results. J Comput Aided Mol Des 22(3-4):257–267

    Article  CAS  Google Scholar 

  14. Ballester PJ, Finn PW, Richards WG (2009) Ultrafast shape recognition: evaluating a new ligand-based virtual screening technology. J Mol Graph Model 27:836–845

    Article  CAS  Google Scholar 

  15. Ballester PJ, Richards WG (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. J Comput Chem 28:1711–1723

    Article  CAS  Google Scholar 

  16. Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG (2010) Prospective virtual screening with ultrafast shape recognition: the identification of novel inhibitors of arylamine n-acetyltransferases. J R Soc Interface 7(43):335–342

    Article  CAS  Google Scholar 

  17. Armstrong MS, Morris GM, Finn PW, Sharma R, Richards WG (2009) Molecular similarity including chirality. J Mol Graph Model 28:368–370

    Article  CAS  Google Scholar 

  18. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801

    Article  CAS  Google Scholar 

  19. Hawkins GD, Giesen DJ, Lynch GC, Chambers CC, Rossi I, Storer JW, Li J, Zhu T, Thompson JD, Winget P, Lynch BJ, Rinaldi D, Liotard DA, Cramer CJ, Truhlar DG (2003) AMSOL-version 7.1. University of Minnesota, Minneapolis

    Google Scholar 

  20. Liotard DA, Healy EF, Ruiz JM, Dewar MJS (1989) AMPAC-version 2.1

  21. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3288

    Article  CAS  Google Scholar 

  22. Dewar MJS, Zoebisch EG, Healy F, Stewart JJP (1985) AM1: a new general purpose quantum mechanical molecular model. J Am Chem Soc 107(13):3902–3909 doi:10.1021/ja00299a024

    Article  CAS  Google Scholar 

  23. Halgren TA (1996) The merck force field. J Comp Chem 17:490–641

    Article  CAS  Google Scholar 

  24. Chemical Computing Group, Montreal, Canada, MOE 2008.10 http://www.chemcomp.com

  25. Armstrong MS, Morris GM, Finn PW, Sharma R, Moretti L, Cooper RI, Richards WG, DUD datasets with new partial charges, http://www.inhibox.com/dud

  26. Cheeseright TJ, Mackey MD, Melville JL, Vinter JG (2008) Fieldscreen: virtual screening using molecular fields: application to the DUD data set. J Chem Inf Model 48(11):2108–2117

    Article  CAS  Google Scholar 

  27. DUD: A Directory of Useful Decoys, http://www.dud.docking.org/r2/ accessed on 31 March 2010

  28. Good AC, Oprea TI (2008) Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?. J Comput Aided Mol Des 22:169–178

    Article  CAS  Google Scholar 

  29. Cannon EO, Nigsch F, Mitchell JBO (2008) A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem Central J 2(3), doi:10.1186/1752-153X-2-3

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Correspondence to M. Stuart Armstrong.

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This research was financed in part by the 6th Framework Program of the European Union (DeZnIT, Project Number 037303) and by the National Foundation for Cancer Research (NFCR).

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Armstrong, M.S., Morris, G.M., Finn, P.W. et al. ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. J Comput Aided Mol Des 24, 789–801 (2010). https://doi.org/10.1007/s10822-010-9374-0

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