Journal of Computer-Aided Molecular Design

, Volume 26, Issue 1, pp 125–134 | Cite as

The errors of our ways: taking account of error in computer-aided drug design to build confidence intervals for our next 25 years



The future of the advancement as well as the reputation of computer-aided drug design will be guided by a more thorough understanding of the domain of applicability of our methods and the errors and confidence intervals of their results. The implications of error in current force fields applied to drug design are given are given as an example. Even as our science advances and our hardware become increasingly more capable, our software will be perhaps the most important aspect in this realization. Some recommendations for the future are provided. Education of users is essential for proper use and interpretation of computational results in the future.


Error Precision Force fields Computational chemistry Drug discovery Drug design Computer-aided drug design Molecular modeling Molecular dynamics Crystal structure prediction 



Many thanks to Marvin Waldman, Anthony Nicholls, Robert Clark, and Yvonne Martin for an insightful review of and contributions to the manuscript. Thanks to David Mobley and John Chodera for sharing their results. The author is indebted to Donald Williams, Marvin Waldman, Sarah Price, Carl Ewig, Arnold Hagler, Shneior Lifson, Peter Kollman, Jay Ponder, Alexander MacKerell, Bernard Brooks, and many others for teaching him about the underbelly of force fields. My appreciation to Anthony Nicholls, Marvin Waldman, William Swope, Julia Rice, Richard Friesner and Thomas Halgren for always invigorating discussions.


  1. 1.
    Head MS (2010) Docking: a domesday report. In: Merz KM, Ringe D, Reynolds CH (eds) Structure and ligand-based drug discovery. Cambridge University Press, Cambridge, pp 98–119CrossRefGoogle Scholar
  2. 2.
    Skillman A, Geballe M, Nicholls A (2010) SAMPL2 challenge: prediction of solvation energies and tautomer ratios. J Comput Aided Mol Des 24(4):257–258CrossRefGoogle Scholar
  3. 3.
    Carlson HA, Dunbar JB (2011) A Call to Arms: what you can do for computational drug discovery. J Chem Inf Model 51(9):2025–2026CrossRefGoogle Scholar
  4. 4.
    Bardwell DA, Adjiman CS, Arnautova YA, Bartashevich E, Boerrigter SXM, Braun DE, Cruz-Cabeza AJ, Day GM, Della Valle RG, Desiraju GR, van Eijck BP, Facelli JC, Ferraro MB, Grillo D, Habgood M, Hofmann DWM, Hofmann F, Jose KVJ, Karamertzanis PG, Kazantsev AV, Kendrick J, Kuleshova LN, Leusen FJJ, Maleev AV, Misquitta AJ, Mohamed S, Needs RJ, Neumann MA, Nikylov D, Orendt AM, Pal R, Pantelides CC, Pickard CJ, Price LS, Price SL, Scheraga HA, van de Streek J, Thakur TS, Tiwari S, Venuti E, Zhitkov IK (2011) Towards crystal structure prediction of complex organic compounds—a report on the fifth blind test. Acta Crystallogr Sect B, 67. doi: 10.1107/S0108768111042868
  5. 5.
    Day GM, Cooper TG, Cruz-Cabeza AJ, Hejczyk KE, Ammon HL, Boerrigter SXM, Tan JS, Della Valle RG, Venuti E, Jose J, Gadre SR, Desiraju GR, Thakur TS, van Eijck BP, Facelli JC, Bazterra VE, Ferraro MB, Hofmann DWM, Neumann MA, Leusen FJJ, Kendrick J, Price SL, Misquitta AJ, Karamertzanis PG, Welch GWA, Scheraga HA, Arnautova YA, Schmidt MU, van de Streek J, Wolf AK, Schweizer B (2009) Significant progress in predicting the crystal structures of small organic molecules—a report on the fourth blind test. Acta Crystallogr Sect B, 65. doi: 10.1107/S0108768109004066
  6. 6.
    Fernández-Recio J, Sternberg MJE (2010) The 4th meeting on the critical assessment of predicted interaction (CAPRI) held at the Mare Nostrum, Barcelona. Proteins: struct funct and bioinform 78(15):3065–3066Google Scholar
  7. 7.
    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507CrossRefGoogle Scholar
  8. 8.
    Moult J, Fidelis K, Kryshtafovych A, Tramontano A (2011) Critical assessment of methods of protein structure prediction (CASP)—round IX. Proteins: Struct Funct and Bioinform 79(S10):1–5Google Scholar
  9. 9.
    Muchmore SW, Debe DA, Metz JT, Brown SP, Martin YC, Hajduk PJ (2008) Application of belief theory to similarity data fusion for use in analog searching and lead hopping. J Chem Inf Mode 48(5):941–948CrossRefGoogle Scholar
  10. 10.
    Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2005) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931CrossRefGoogle Scholar
  11. 11.
    Tang KT, Toennies JP (2003) The van der Waals potentials between all the rare gas atoms from He to Rn. Chemphyschem 118(11):4976–4983Google Scholar
  12. 12.
    Waldman M, Hagler AT (1993) New combining rules for rare gas van der Waals parameters. J Comput Chem 14(9):1077–1084CrossRefGoogle Scholar
  13. 13.
    Mobley DL, Bayly CI, Cooper MD, Shirts MR, Dill KA (2009) Small molecule hydration free energies in explicit solvent: an extensive test of fixed-charge atomistic simulations. J Chem Theory Comput 5(2):350–358CrossRefGoogle Scholar
  14. 14.
    Williams DE (1994) Failure of net atomic charge models to represent the van der Waals envelope electric potential of n-alkanes. J Comput Chem 15(7):719–732CrossRefGoogle Scholar
  15. 15.
    Mobley DL, Dumont E, Chodera JD, Dill KA (2007) Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J Phys Chem B 111(9):2242–2254CrossRefGoogle Scholar
  16. 16.
    Mobley DL, Graves AP, Chodera JD, McReynolds AC, Shoichet BK, Dill KA (2007) Predicting absolute ligand binding free energies to a simple model site. J Mol Biol 371(4):1118–1134CrossRefGoogle Scholar
  17. 17.
    Mobley DL (2011) Personal communication on the sensitivity of hydration free energy to charge variationGoogle Scholar
  18. 18.
    Tejwani RW, Davis ME, Anderson BD, Stouch TR (2011) An atomic and molecular view of the depth dependence of the free energies of solute transfer from water into lipid bilayers. Mol Pharm 8(6):2204–2215CrossRefGoogle Scholar
  19. 19.
    Tejwani RW, Davis ME, Anderson BD, Stouch TR (2011) Functional group dependence of solute partitioning to various locations within a DOPC bilayer: a comparison of molecular dynamics simulations with experiment. J Pharm Sci 100(6):2136–2146CrossRefGoogle Scholar
  20. 20.
    Case FH, Chaka A, Moore JD, Mountain RD, Ross RB, Shen VK, Stahlberg EA (2011) The sixth industrial fluid properties simulation challenge. Fluid Phase Equilib 310(1–2):1–3CrossRefGoogle Scholar
  21. 21.
    Williams DE, Gao D (1997) Effects of molecular electric potential and anisotropic atomic repulsion in the dichlorine dimer and crystalline chlorine. Inorg Chem 36(5):782–788CrossRefGoogle Scholar
  22. 22.
    Day GM, Price SL (2003) A nonempirical anisotropic atom−atom model potential for chlorobenzene crystals. J Am Chem Soc 125(52):16434–16443CrossRefGoogle Scholar
  23. 23.
    Sherrill CD, Sumpter BG, Sinnokrot MO, Marshall MS, Hohenstein EG, Walker RC, Gould IR (2009) Assessment of standard force field models against high-quality ab initio potential curves for prototypes of π–π, CH/π, and SH/π interactions. J Comput Chem 30(14):2187–2193Google Scholar
  24. 24.
    Segall M, Champness E, Leeding C, Lilien R, Mettu R, Stevens B (2011) Applying medicinal chemistry transformations and multiparameter optimization to guide the search for high-quality leads and candidates. J Chem Inf Model 51(11):2967–2976Google Scholar
  25. 25.
    Swann SL, Brown SP, Muchmore SW, Patel H, Merta P, Locklear J, Hajduk PJ (2011) A unified, probabilistic framework for structure- and ligand-based virtual screening. J Med Chem 54(5):1223–1232CrossRefGoogle Scholar
  26. 26.
    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 (2011) Molecular shape and medicinal chemistry: a perspective. J Med Chem 53(10):3862–3886CrossRefGoogle Scholar
  27. 27.
    Stouch, TR (2011) The intricacies of pharmaceutical data: what is required to properly understand it. OpenEye Scientific Software, EuroCUP meeting, Dublin, Ireland, September 2011,

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Science For Solutions, LLCWest WindsorUSA

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