Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol–water log P blind challenge

  • Shuzhe Wang
  • Sereina RinikerEmail author


The in silico prediction of partition coefficients is an important task in computer-aided drug discovery. In particular the octanol–water partition coefficient is used as surrogate for lipophilicity. Various computational approaches have been proposed, ranging from simple group-contribution techniques based on the 2D topology of a molecule to rigorous methods based molecular dynamics (MD) or quantum chemistry. In order to balance accuracy and computational cost, we recently developed the MD fingerprints (MDFPs), where the information in MD simulations is encoded in a floating-point vector, which can be used as input for machine learning (ML). The MDFP-ML approach was shown to perform similarly to rigorous methods while being substantially more efficient. Here, we present the application of MDFP-ML for the prediction of octanol–water partition coefficients in the SAMPL6 blind challenge. The underlying computational pipeline is made freely available in form of the MDFPtools package.


Molecular dynamics Machine learning SAMPL6 



The authors gratefully acknowledge financial support by the Swiss National Science Foundation (Grant Number 200021-178762) and by ETH Zurich (ETH-34 17-2). They further acknowledge SAMPL NIH Grant 1R01GM124270-01A1 for support of the experimental work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Electronic supplementary material 1 (TXT 1576 kb)
10822_2019_252_MOESM2_ESM.txt (349 kb)
Electronic supplementary material 2 (TXT 349 kb)


  1. 1.
    Waring MJ (2016) Expert Opin Drug Discov 5:235CrossRefGoogle Scholar
  2. 2.
    Meyer H (1899) Naunyn-Schmiedeberg’s Arch Pharmacol 42:109CrossRefGoogle Scholar
  3. 3.
    Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) J Pharm Sci 98:861CrossRefGoogle Scholar
  4. 4.
    Zwanzig RW (1954) J Chem Phys 22:1420CrossRefGoogle Scholar
  5. 5.
    Klamt A, Eckert F, Arlt W (2010) Annu Rev Chem Biomol Eng 1:101CrossRefGoogle Scholar
  6. 6.
    Klamt A, Eckert F, Reinisch J, Wichmann K (2016) J Comput Aided Drug Des 30:959CrossRefGoogle Scholar
  7. 7.
    Riniker S (2017) J Chem Inf Mod 57:726CrossRefGoogle Scholar
  8. 8.
    Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) J Comput Aided Mol Des 30:927CrossRefGoogle Scholar
  9. 9.
    Isik M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL (2019) J Comput Aided Mol Des, submittedGoogle Scholar
  10. 10.
    Bayly CI, McKay D, Truchon JF (2011) An informal AMBER small molecule force field: parm@frosst.
  11. 11.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25:1157CrossRefGoogle Scholar
  12. 12.
    Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang LP, Shukla D et al (2012) J Chem Theory Comput 9:461CrossRefGoogle Scholar
  13. 13.
    Schmid N, Christ CD, Christen M, Eichenberger AP, van Gunsteren WF (2012) Comput Phys Commun 183:890CrossRefGoogle Scholar
  14. 14.
    Darden T, York D, Pedersen L (1993) J Chem Phys 98:10089CrossRefGoogle Scholar
  15. 15.
    Tironi I, Sperb R, Smith PE, van Gunsteren WF (1995) J Chem Phys 102:5451CrossRefGoogle Scholar
  16. 16.
    Riniker S, Landrum GA (2015) J Chem Inf Mod 55:2652CrossRefGoogle Scholar
  17. 17.
    Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) J Chem Inf Mod 50:572CrossRefGoogle Scholar
  18. 18.
    Eastman P (2019) PDBFixer.
  19. 19.
    Mobley DL, Bannan CC, Rizzi A, Bayly CI, Chodera JD, Lim VT, Lim NM, Beauchamp KA, Slochower DR, Shirts MR, Gilson MK, Eastman PK (2018) J Chem Theory Comput 14:6076CrossRefGoogle Scholar
  20. 20.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21:132CrossRefGoogle Scholar
  21. 21.
    Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23:1623CrossRefGoogle Scholar
  22. 22.
    Case DA, Botello-Smith RMBW, Cerutti DS, Cheatham TE, Darden TA, Duke RE et al (2016) AMBER 16.
  23. 23.
    Bleiziffer P, Schaller K, Riniker S (2018) J Chem Inf Mod 58:579CrossRefGoogle Scholar
  24. 24.
    Swails J, Hernandez C, Mobley DL, Nguyen H, Wang LP, Janowski P (2010) ParmEd.
  25. 25.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) Comput Phys Commun 91:43CrossRefGoogle Scholar
  26. 26.
    McGibbon RT, Beauchamp KA, Harrigan MP, Klein C, Swails JM, Hernández CX, Schwantes CR, Wang LP, Lane TJ, Pande VS (2015) Biophys J 109:1528CrossRefGoogle Scholar
  27. 27.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) J Mach Learn Res 12:2825Google Scholar
  28. 28.
  29. 29.
    Mobley DL, Guthrie JP (2014) J Comput Aided Mol Des 28:711CrossRefGoogle Scholar
  30. 30.
    Matos GDR, Kyu DY, Loeffler HH, Chodera JD, Shirts MR, Mobley DL (2017) J Chem Eng Data 62:1559CrossRefGoogle Scholar
  31. 31.
    Marenich AV, Kelly CP, Thompson JD, Hawkins GD, Chambers CC, Giesen DJ, Winget P, Cramer CJ, Truhlar DG (2012) Minnesota solvation database, version 2012Google Scholar
  32. 32.
    Sushko I, Novotarskyi S, Körner R, Pandey AK, Rupp M, Teetz W, Brandmaier S, Abdelaziz A, Prokopenko VV, Tanchuk VY, Todeschini R, Varnek A, Marcou G, Ertl P, Potemkin V, Grishina M, Gasteiger J, Schwab C, Baskin II, Palyulin VA, Radchenko EV, Welsh WJ, Kholodovych V, Chekmarev D, Cherkasov A, de Sousa JA, Zhang QY, Bender A, Nigsch F, Patiny L, Williams A, Tkachenko V, Tetko IV (2011) J Comput Aided Mol Des 25:533CrossRefGoogle Scholar
  33. 33.
    Tibshirani R (1996) J R Stat Soc B 58:267Google Scholar
  34. 34.
    Friedman JH (2002) Comput Stat Data Anal 38:367CrossRefGoogle Scholar
  35. 35.
    Smith JS, Isayev O, Roitberg AE (2017) Chem Sci 8:3192CrossRefGoogle Scholar
  36. 36.
    Chen T, Guestrin C (2016) Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (ACM, 2016), pp 785–794Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory of Physical ChemistryETH ZurichZurichSwitzerland

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