Skip to main content
Springer Nature Link
Log in

Side chain virtual screening of matched molecular pairs: a PDB-wide and ChEMBL-wide analysis

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Optimization in medicinal chemistry often involves designing replacements for a section of a molecule which aim to retain potency while improving other properties of the compound. In this study, we perform a retrospective analysis using a number of computational methods to identify active side chains amongst a pool of random decoy side chains, mimicking a similar procedure that might be undertaken in a real medicinal chemistry project. We constructed a dataset derived from public ChEMBL and PDB data by identifying all ChEMBL assays where at least one of the compounds tested has also been co-crystallized in the PDB. Additionally, we required that there be at least ten active compounds tested in the same ChEMBL assay that are matched molecular pairs to the crystallized ligand. Using the compiled dataset consisting of sets of compounds from 402 assays, we have tested a number of methods for scoring side chains including Spark, a bioisostere replacement tool from Cresset, molecular docking using Glide from Schrodinger, docking with Smina, as well as other methods. In this work, we present a comparison of the performance of these methods in discriminating active side chains from decoys as well as recommendations for circumstances when different methods should be used.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ripphausen P, Nisius B, Peltason L, Bajorath J (2010) Quo vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53(24):8461–8467. doi:https://doi.org/10.1021/jm101020z

    Article  CAS  PubMed  Google Scholar 

  2. Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508. doi:https://doi.org/10.1021/ci600426e

    Article  CAS  PubMed  Google Scholar 

  3. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801. doi:https://doi.org/10.1021/jm0608356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Venkatraman V, Perez-Nueno VI, Mavridis L, Ritchie DW (2010) Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods. J Chem Inf Model 50(12):2079–2093. doi:https://doi.org/10.1021/ci100263p

    Article  CAS  PubMed  Google Scholar 

  5. Riniker S, Landrum GA (2013) Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform 5(1):26. doi:https://doi.org/10.1186/1758-2946-5-26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Leach AG, Jones HD, Cosgrove DA, Kenny PW, Ruston L, MacFaul P, Wood JM, Colclough N, Law B (2006) Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. J Med Chem 49(23):6672–6682. doi:https://doi.org/10.1021/jm0605233

    Article  CAS  PubMed  Google Scholar 

  7. Griffen E, Leach AG, Robb GR, Warner DJ (2011) Matched molecular pairs as a medicinal chemistry tool. J Med Chem 54(22):7739–7750. doi:https://doi.org/10.1021/jm200452d

    Article  CAS  PubMed  Google Scholar 

  8. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Kruger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42(Database issue):D1083–D1090. doi:https://doi.org/10.1093/nar/gkt1031

    Article  CAS  PubMed  Google Scholar 

  9. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(Database issue):D1100–D1107. doi:https://doi.org/10.1093/nar/gkr777

    Article  CAS  PubMed  Google Scholar 

  10. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrian-Uhalte E, Davies M, Dedman N, Karlsson A, Magarinos MP, Overington JP, Papadatos G, Smit I, Leach AR (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):D945–D954. doi:https://doi.org/10.1093/nar/gkw1074

    Article  CAS  PubMed  Google Scholar 

  11. Erl Wood Cheminformatics nodes for KNIME (2017)

  12. Wagener M, Lommerse JP (2006) The quest for bioisosteric replacements. J Chem Inf Model 46(2):677–685. doi:https://doi.org/10.1021/ci0503964

    Article  CAS  PubMed  Google Scholar 

  13. Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50(3):339–348. doi:https://doi.org/10.1021/ci900450m

    Article  CAS  PubMed  Google Scholar 

  14. Papadatos G, Alkarouri M, Gillet VJ, Willett P, Kadirkamanathan V, Luscombe CN, Bravi G, Richmond NJ, Pickett SD, Hussain J, Pritchard JM, Cooper AW, Macdonald SJ (2010) Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity. J Chem Inf Model 50(10):1872–1886. doi:https://doi.org/10.1021/ci100258p

    Article  CAS  PubMed  Google Scholar 

  15. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. doi:https://doi.org/10.1021/jm300687e

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Landrum G RDKit: Open Source cheminformatics

  17. Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53(8):1893–1904. doi:https://doi.org/10.1021/ci300604z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Smina Apr 2 2016 build. https://sourceforge.net/projects/smina/

  19. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. doi:https://doi.org/10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  20. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759. doi:https://doi.org/10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  21. Schrödinger Release 2017-2. Protein Preparation Wizard; Epik, Schrödinger LLC, New York NY, 2016; Impact, Schrödinger, LLC, New York, NY, 2016; Prime, Schrödinger, LLC, New York, NY, 2016.

  22. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234. https://doi.org/10.1007/s10822-013-9644-8

    Article  CAS  PubMed  Google Scholar 

  23. Spark, 10.4.0, Cresset®, Litlington, Cambridgeshire, UK; http://www.cresset-group.com/spark/;

  24. Cheeseright T, Mackey M, Rose S, Vinter A (2006) Molecular field extrema as descriptors of biological activity: definition and validation. J Chem Inf Model 46(2):665–676. https://doi.org/10.1021/ci050357s

    Article  CAS  PubMed  Google Scholar 

  25. Watts KS, Dalal P, Murphy RB, Sherman W, Friesner RA, Shelley JC (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50(4):534–546. https://doi.org/10.1021/ci100015j

    Article  CAS  PubMed  Google Scholar 

  26. Daylight Theory Manual http://www.daylight.com/dayhtml/doc/theory/index.pdf

  27. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Koes DR. https://github.com/dkoes/asacalc/blob/master/asacalc.cpp. Accessed 2016

  29. Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39(15):2887–2893. doi:https://doi.org/10.1021/jm9602928

    Article  CAS  PubMed  Google Scholar 

  30. Erickson JA, Jalaie M, Robertson DH, Lewis RA, Vieth M (2004) Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J Med Chem 47(1):45–55. doi:https://doi.org/10.1021/jm030209y

    Article  CAS  PubMed  Google Scholar 

  31. Abel R, Wang L, Harder ED, Berne BJ, Friesner RA (2017) Advancing drug discovery through enhanced free energy calculations. Acc Chem Res 50(7):1625–1632. https://doi.org/10.1021/acs.accounts.7b00083

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n°612347. The authors wish to acknowledge Lewis Vidler for constructive discussion and feedback and Jeremy Desaphy for the prepared PDB structures.

Author information

Authors and Affiliations

  1. Computational Chemistry and Cheminformatics, Eli Lilly and Company Inc., San Diego, CA, USA

    Matthew P. Baumgartner

  2. Computational Chemistry and Cheminformatics, Eli Lilly and Company Ltd., Windlesham, UK

    David A. Evans

Authors
  1. Matthew P. Baumgartner
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. David A. Evans
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to David A. Evans.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baumgartner, M.P., Evans, D.A. Side chain virtual screening of matched molecular pairs: a PDB-wide and ChEMBL-wide analysis. J Comput Aided Mol Des 34, 953–963 (2020). https://doi.org/10.1007/s10822-020-00313-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-020-00313-1

Keywords