Skip to main content
SpringerLink
Log in

Assessing the accuracy of octanol–water partition coefficient predictions in the SAMPL6 Part II log P Challenge

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

Abstract

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol–water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p\({K}_{{\rm a}}\) Challenge, which asked participants to predict p\({K}_{{\rm a}}\) values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol–water log P predictions in SAMPL6 Challenge was higher than cyclohexane–water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol–water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Code and data availability

All SAMPL6 log P challenge instructions, submissions, experimental data and analysis are available at https://github.com/samplchallenges/SAMPL6/tree/master/physical_properties/logP. An archive copy of SAMPL6 GitHub Repository log P challenge directory is also available in the Supplementary Documents bundle (Electronic Supplementary Material 2). Some useful files from this repository are highlighted below. (a) Table of participants and their submission filenames: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/predictions/SAMPL6-user-map-logP.csv. (b) Table of methods including submission IDs, method names, participant assigned method category, and reassigned method categories: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/predictions/SAMPL6-logP-method-map.csv. (c) Submission files of prediction sets: https://github.com/samplchallenges/SAMPL6/tree/master/physical_properties/logP/predictions/submission_files. (d) Python analysis scripts and outputs: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/analysis_with_reassigned_categories/. (e) Table of performance statistics calculated for all methods: https://github.com/samplchallenges/SAMPL6/blob/master/physical_properties/logP/analysis_with_reassigned_categories/analysis_outputs_withrefs/StatisticsTables/statistics.csv.

Abbreviations

SAMPL:

Statistical Assessment of the Modeling of Proteins and Ligands

log P :

\(\hbox {log}_{10}\) of the organic solvent-water partition coefficient (\(K_{ow}\)) of neutral species

log D :

\(\hbox {log}_{10}\) of organic solvent-water distribution coefficient (\(D_{ow}\))

p\({K}_{{\rm a}}\) :

\(-\hbox {log}_{10}\) of the acid dissociation equilibrium constant

SEM:

Standard error of the mean

RMSE:

Root mean squared error

MAE:

Mean absolute error

\(\tau\) :

Kendall’s rank correlation coefficient (Tau)

R2 :

Coefficient of determination (R\(^{2}\))

QM:

Quantum mechanics

MM:

Molecular mechanics

References

  1. Rustenburg AS, Dancer J, Lin B, Feng JA, Ortwine DF, Mobley DL, Chodera JD (2016) Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge. J Comput Aided Mol Des 30(11):945–958. https://doi.org/10.1007/s10822-016-9971-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30(11):927–944. https://doi.org/10.1007/s10822-016-9954-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kollman PA (1996) Advances and continuing challenges in achieving realistic and predictive simulations of the properties of organic and biological molecules. Acc Chem Res 29(10):461–469. https://doi.org/10.1021/ar9500675

    Article  CAS  Google Scholar 

  4. Best SA, Merz KM, Reynolds CH (1999) Free energy perturbation study of octanol/water partition coefficients: comparison with continuum GB/SA calculations. J Phys Chem B 103(4):714–726. https://doi.org/10.1021/jp984215v

    Article  CAS  Google Scholar 

  5. Chen B, Siepmann JI (2006) Microscopic structure and solvation in dry and wet octanol. J Phys Chem B 110(8):3555–3563. https://doi.org/10.1021/jp0548164

    Article  CAS  PubMed  Google Scholar 

  6. Lyubartsev AP, Jacobsson SP, Sundholm G, Laaksonen A (2001) Solubility of organic compounds in water/octanol systems. A expanded ensemble molecular dynamics simulation study of log P parameters. J Phys Chem B 105(32):7775–7782. https://doi.org/10.1021/jp0036902

    Article  CAS  Google Scholar 

  7. DeBolt SE, Kollman PA (1995) Investigation of structure, dynamics, and solvation in 1-octanol and its water-saturated solution: molecular dynamics and free-energy perturbation studies. J Am Chem Soc 117(19):5316–5340. https://doi.org/10.1021/ja00124a015

    Article  CAS  Google Scholar 

  8. Işık M, Levorse D, Rustenburg AS, Ndukwe IE, Wang H, Wang X, Reibarkh M, Martin GE, Makarov AA, Mobley DL, Rhodes T, Chodera JD (2018) pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J Comput Aided Mol Des 32(10):1117–1138. https://doi.org/10.1007/s10822-018-0168-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD (2019) Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00271-3

    Article  PubMed  Google Scholar 

  10. Marenich AV, Cramer CJ, Truhlar DG (2009) Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J Phys Chem B 113(18):6378–6396. https://doi.org/10.1021/jp810292n

    Article  CAS  PubMed  Google Scholar 

  11. Marenich AV, Cramer CJ, Truhlar DG (2009) Performance of SM6, SM8, and SMD on the SAMPL1 test set for the prediction of small-molecule solvation free energies. J Phys Chem B 113(14):4538–4543. https://doi.org/10.1021/jp809094y

    Article  CAS  PubMed  Google Scholar 

  12. Ribeiro RF, Marenich AV, Cramer CJ, Truhlar DG (2010) Prediction of SAMPL2 aqueous solvation free energies and tautomeric ratios using the SM8, SM8AD, and SMD solvation models. J Comput Aided Mol Des 24(4):317–333. https://doi.org/10.1007/s10822-010-9333-9

    Article  CAS  PubMed  Google Scholar 

  13. Marenich AV, Cramer CJ, Truhlar DG (2013) Generalized born solvation model SM12. J Chem Theory Comput 9(1):609–620. https://doi.org/10.1021/ct300900e

    Article  CAS  PubMed  Google Scholar 

  14. Loschen C, Reinisch J, Klamt A (2019) COSMO-RS based predictions for the SAMPL6 logP challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00259-z

    Article  PubMed  Google Scholar 

  15. Klamt A, Eckert F, Reinisch J, Wichmann K (2016) Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set. J Comput Aided Mol Des 30(11):959–967. https://doi.org/10.1007/s10822-016-9927-y

    Article  CAS  PubMed  Google Scholar 

  16. Klamt A, Eckert F, Diedenhofen M (2009) Prediction of the free energy of hydration of a challenging set of pesticide-like compounds. J Phys Chem B 113(14):4508–4510. https://doi.org/10.1021/jp805853y

    Article  CAS  PubMed  Google Scholar 

  17. Klamt A (1995) Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem 99(7):2224–2235. https://doi.org/10.1021/j100007a062

    Article  CAS  Google Scholar 

  18. Klamt A, Jonas V, Bürger T, Lohrenz JCW (1998) Refinement and parametrization of COSMO-RS. J Phys Chem A 102(26):5074–5085. https://doi.org/10.1021/jp980017s

    Article  CAS  Google Scholar 

  19. Tielker N, Tomazic D, Heil J, Kloss T, Ehrhart S, Güssregen S, Schmidt KF, Kast SM (2016) The SAMPL5 challenge for embedded-cluster integral equation theory: solvation free energies, aqueous pKa, and cyclohexane-water log D. J Comput Aided Mol Des 30(11):1035–1044. https://doi.org/10.1007/s10822-016-9939-7

    Article  CAS  PubMed  Google Scholar 

  20. Tielker N, Eberlein L, Güssregen S, Kast SM (2018) The SAMPL6 challenge on predicting aqueous pKa values from EC-RISM theory. J Comput Aided Mol Des 32(10):1151–1163. https://doi.org/10.1007/s10822-018-0140-z

    Article  CAS  PubMed  Google Scholar 

  21. Luchko T, Blinov N, Limon GC, Joyce KP, Kovalenko A (2016) SAMPL5: 3D-RISM partition coefficient calculations with partial molar volume corrections and solute conformational sampling. J Comput Aided Mol Des 30(11):1115–1127. https://doi.org/10.1007/s10822-016-9947-7

    Article  CAS  PubMed  Google Scholar 

  22. Tielker N, Tomazic D, Eberlein L, Güssregen S, Kast SM (2020) The SAMPL6 challenge on predicting octanol-water partition coefficients from ECRISM theory. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00283-4

    Article  PubMed  Google Scholar 

  23. Beglov D, Roux B (1997) An integral equation to describe the solvation of polar molecules in liquid water. J Phys Chem B 101(39):7821–7826. https://doi.org/10.1021/jp971083h

    Article  CAS  Google Scholar 

  24. Kovalenko A, Hirata F (1998) Three-dimensional density profiles of water in contact with a solute of arbitrary shape: a RISM approach. Chem Phys Lett 290(1–3):237–244. https://doi.org/10.1016/S0009-2614(98)00471-0

    Article  CAS  Google Scholar 

  25. Tielker N, Eberlein L, Güssregen S, Kast SM (2018) The SAMPL6 challenge on predicting aqueous pKa values from EC-RISM theory. J Comput Aid Mol Des. 32(10):1151–1163. https://doi.org/10.1007/s10822-018-0140-z

    Article  CAS  Google Scholar 

  26. Kloss T, Heil J, Kast SM (2008) Quantum chemistry in solution by combining 3D integral equation theory with a cluster embedding approach. J Phys Chem B 112(14):4337–4343. https://doi.org/10.1021/jp710680m

    Article  CAS  PubMed  Google Scholar 

  27. Darve E, Pohorille A (2001) Calculating free energies using average force. J Chem Phys 115(20):9169–9183. https://doi.org/10.1063/1.1410978

    Article  CAS  Google Scholar 

  28. Comer J, Gumbart JC, Hénin J, Lelièvre T, Pohorille A, Chipot C (2015) The adaptive biasing force method: everything you always wanted to know but were afraid to ask. J Phys Chem B 119(3):1129–1151. https://doi.org/10.1021/jp506633n

    Article  CAS  PubMed  Google Scholar 

  29. Nieto-Draghi C, Fayet G, Creton B, Rozanska X, Rotureau P, de Hemptinne JC, Ungerer P, Rousseau B, Adamo C (2015) A general guidebook for the theoretical prediction of physicochemical properties of chemicals for regulatory purposes. Chem Rev 115(24):13093–13164. https://doi.org/10.1021/acs.chemrev.5b00215

    Article  CAS  PubMed  Google Scholar 

  30. Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) Calculation of molecular lipophilicity: state-of-the-art and comparison of LogP methods on more than 96,000 compounds. J Pharm Sci 98(3):861–893. https://doi.org/10.1002/jps.21494

    Article  CAS  PubMed  Google Scholar 

  31. Eros D, Kovesdi I, Orfi L, Keri G (2002) Reliability of logP predictions based on calculated molecular descriptors: a critical review. Curr Med Chem 9(20):1819–1829. https://doi.org/10.2174/0929867023369042

    Article  CAS  PubMed  Google Scholar 

  32. Ghose AK, Crippen GM (1986) Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comput Chem 7(4):565–577. https://doi.org/10.1002/jcc.540070419

    Article  CAS  Google Scholar 

  33. Ghose AK, Crippen GM (1987) Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J Chem Inf Comput Sci 27(1):21–35. https://doi.org/10.1021/ci00053a005

    Article  CAS  PubMed  Google Scholar 

  34. Ghose AK, Viswanadhan VN, Wendoloski JJ (1998) Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods. J Phys Chem A 102(21):3762–3772. https://doi.org/10.1021/jp980230o

    Article  CAS  Google Scholar 

  35. Wildman SA, Crippen GM (1999) Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci 39(5):868–873. https://doi.org/10.1021/ci990307l

    Article  CAS  Google Scholar 

  36. Wang R, Fu Y, Lai L (1997) A new atom-additive method for calculating partition coefficients. J Chem Inf Comput Sci 37(3):615–621. https://doi.org/10.1021/ci960169p

    Article  CAS  Google Scholar 

  37. Wang R, Gao Y, Lai L (2000) Calculating partition coefficient by atom-additive method. Perspect Drug Disc. 19(1):47–66. https://doi.org/10.1023/A:1008763405023

    Article  CAS  Google Scholar 

  38. Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L (2007) Computation of octanol-water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model 47(6):2140–2148. https://doi.org/10.1021/ci700257y

    Article  CAS  PubMed  Google Scholar 

  39. Leo AJ, Hoekman D (2000) Calculating log P(Oct) with no missing fragments; the problem of estimating new interaction parameters. Perspect Drug Discov Des 18(1):19–38. https://doi.org/10.1023/A:1008739110753

    Article  CAS  Google Scholar 

  40. Leo AJ (1993) Calculating log Poct from structures. Chem Rev 93(4):1281–1306

    Article  CAS  Google Scholar 

  41. Leo A (1983) The octanol-water partition coefficient of aromatic solutes: the effect of electronic interactions, alkyl chains, hydrogen bonds, and ortho-substitution. J Chem Soc Perkin Trans 2(6):825–838. https://doi.org/10.1039/P29830000825

    Article  Google Scholar 

  42. Sangster J (1989) Octanol-water partition coefficients of simple organic compounds. J Phys Chem Ref Data 18(3):1111–1229

    Article  CAS  Google Scholar 

  43. Klopman G, Li JY, Wang S, Dimayuga M (1994) Computer automated Log P calculations based on an extended group contribution approach. J Chem Inf Model 34(4):752–781. https://doi.org/10.1021/ci00020a009

    Article  CAS  Google Scholar 

  44. Petrauskas AA, Kolovanov EA (2000) ACD/Log P method description. Persect Drug Discov 19(1):99–116. https://doi.org/10.1023/A:1008719622770

    Article  CAS  Google Scholar 

  45. Meylan WM, Howard PH (1995) Atom/fragment contribution method for estimating octanol-water partition coefficients. J Pharm Sci 84(1):83–92. https://doi.org/10.1002/jps.2600840120

    Article  CAS  PubMed  Google Scholar 

  46. Moriguchi I, Hirono S, Liu Q, Nakagome I, Matsushita Y (1992) Simple method of calculating octanol/water partition coefficient. Chem Pharm Bull 40(1):127–130. https://doi.org/10.1248/cpb.40.127

    Article  CAS  Google Scholar 

  47. Gombar VK, Enslein K (1996) Assessment of N-octanol/water partition coefficient: when is the assessment reliable? J Chem Inf Comput Sci 36(6):1127–1134. https://doi.org/10.1021/ci960028n

    Article  CAS  PubMed  Google Scholar 

  48. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34(Database Issue):D668–672. https://doi.org/10.1093/nar/gkj067

    Article  CAS  PubMed  Google Scholar 

  49. Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87(11):1123–1124. https://doi.org/10.1021/ed100697w

    Article  CAS  Google Scholar 

  50. NCI open database, August 2006 release. https://cactus.nci.nih.gov/download/nci/

  51. Enhanced NCI database browser 2.2. https://cactus.nci.nih.gov/ncidb2.2/

  52. SRC’s PHYSPROP database. https://www.srcinc.com/what-we-do/environmental/scientific-databases.html

  53. OEDepict Toolkit 2017.Feb.1. OpenEye Scientific Software, Santa Fe. http://www.eyesopen.com

  54. Mobley DL, Işık M, Paluch A, Loschen C, Tielker N, Vöhringer-Martinez E, Nikitin A (2019) The SAMPL6 LogP virtual workshop. Zenodo. https://doi.org/10.5281/zenodo.3518862

    Article  Google Scholar 

  55. Mobley DL, Işık M, Paluch A, Loschen C, Tielker N, Vöhringer-Martinez E, Nikitin A (2019) The SAMPL6 LogP virtual workshop GitHub repository for presentation slides. https://github.com/choderalab/SAMPL6-logP-challenge-virtual-workshop

  56. Mobley DL (2019) SAMPL: its present and future, and some work on the logP challenge. Zenodo. https://doi.org/10.5281/zenodo.3376196

    Article  Google Scholar 

  57. Işık M (2019) SAMPL6 part II partition coefficient challenge overview. Zenodo. https://doi.org/10.5281/zenodo.3386592

    Article  Google Scholar 

  58. Lang BE (2012) Solubility of water in octan-1-Ol from (275 to 369) K. J Chem Eng Data 57(8):2221–2226. https://doi.org/10.1021/je3001427

    Article  CAS  Google Scholar 

  59. Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71(6):525–616. https://doi.org/10.1021/cr60274a001

    Article  CAS  Google Scholar 

  60. Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des 28(3):135–150. https://doi.org/10.1007/s10822-014-9718-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Andrea A, Grinaway P, Parton D, Shirts M, Wang K, Eastman P, Friedrichs M, Pande V, Branson K, Mobley D, Chodera J. YANK: a GPU-accelerated platform for alchemical free energy calculations

  62. Wang K, Chodera JD, Yang Y, Shirts MR (2013) Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics. J Comput Aided Mol Des 27(12):989–1007

    Article  CAS  Google Scholar 

  63. Rizzi A, Chodera J, Naden L, Beauchamp K, Albanese S, Grinaway P, Rustenburg BA, Saladi S, Boehm K (2019) choderalab/yank: 0.24.0-experimental support for online status files. Zenodo. https://doi.org/10.5281/zenodo.2577832

    Article  Google Scholar 

  64. Eastman P (2009) Pande vs. efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J Comput Chem. https://doi.org/10.1002/jcc.21413

    Article  PubMed  PubMed Central  Google Scholar 

  65. Eastman P, Pande V (2010) OpenMM: a hardware-independent framework for molecular simulations. Comput Sci Eng 12(4):34–39. https://doi.org/10.1109/MCSE.2010.27

    Article  CAS  PubMed Central  Google Scholar 

  66. Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang LP, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS (2013) OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J Chem Theory Comput 9(1):461–469. https://doi.org/10.1021/ct300857j

    Article  CAS  PubMed  Google Scholar 

  67. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. https://doi.org/10.1002/jcc.20035

    Article  CAS  PubMed  Google Scholar 

  68. Mobley DL, Bannan CC, Rizzi A, Bayly CI, Chodera JD, Lim VT, Lim NM, Beauchamp KA, Shirts MR, Gilson MK, Eastman PK (2018) Open force field consortium: escaping atom types using direct chemical perception with SMIRNOFF v0.1. bioRxiv. https://doi.org/10.1101/286542

  69. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  70. Wang LP, Martinez TJ, Pande VS (2014) Building force fields: an automatic, systematic, and reproducible approach. J Phys Chem Lett. 5(11):1885–1891. https://doi.org/10.1021/jz500737m

    Article  CAS  PubMed  Google Scholar 

  71. Izadi S, Anandakrishnan R, Onufriev AV (2014) Building water models: a different approach. J Phys Chem Lett 5(21):3863–3871. https://doi.org/10.1021/jz501780a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Shultz MD (2019) Two decades under the influence of the rule of five and the changing properties of approved oral drugs: miniperspective. J Med Chem 62(4):1701–1714. https://doi.org/10.1021/acs.jmedchem.8b00686

    Article  CAS  PubMed  Google Scholar 

  73. Procacci P, Guarnieri G (2019) SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00233-9

    Article  PubMed  Google Scholar 

  74. Riquelme M, Vöhringer-Martinez E (2020) SAMPL6 octanol-water partition coefficients from alchemical free energy calculations with MBIS atomic charges. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00281-6

    Article  PubMed  Google Scholar 

  75. Patel P, Kuntz DM, Jones MR, Brooks B, Wilson A (2020) SAMPL6 LogP challenge: machine learning and quantum mechanical approaches. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00287-0

    Article  PubMed  Google Scholar 

  76. Ouimet JA, Paluch AS (2020) Predicting octanol/water partition coefficients for the SAMPL6 challenge using the SM12, SM8, and SMD solvation models. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00293-2

    Article  PubMed  Google Scholar 

  77. Lui R, Guan D, Matthews S (2020) A comparison of molecular representations for lipophilicity quantitative structure-property relationships with results from the SAMPL6 logP prediction challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00279-0

    Article  PubMed  Google Scholar 

  78. Jones MR, Brooks BR (2020) Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 blind challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00286-1

    Article  PubMed  Google Scholar 

  79. Guan D, Lui R, Matthews S (2020) LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00278-1

    Article  PubMed  Google Scholar 

  80. Arslan E, Findik BK, Aviyente V (2020) A blind SAMPL6 challenge: insight into the octanol-water partition coefficients of drug-like molecules via a DFT approach. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00284-3

    Article  PubMed  Google Scholar 

  81. Fan S, Iorga BI, Beckstein O (2020) Prediction of octanol-water partition coefficients for the SAMPL6-$$ log P$$logP molecules using molecular dynamics simulations with OPLS-AA. J Comput Aided Mol Des, AMBER and CHARMM force fields. https://doi.org/10.1007/s10822-019-00267-z

    Book  Google Scholar 

  82. Wang S, Riniker S (2019) Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol-water log P blind challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00252-6

    Article  PubMed  PubMed Central  Google Scholar 

  83. Krämer A, Hudson PS, Jones MR, Brooks BR (2020) Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water-octanol partition coefficients. J Comput Aided Mol Des (SAMPL6 Part II Special Issue)

  84. Nikitin A (2019) Non-zero Lennard-Jones parameters for the Toukan-Rahman water model: more accurate calculations of the solvation free energy of organic substances. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00256-2

    Article  PubMed  Google Scholar 

  85. Zamora WJ, Pinheiro S, German K, Ràfols C, Curutchet C, Luque FJ (2019) Prediction of the N-octanol/water partition coefficients in the SAMPL6 blind challenge from MST continuum solvation calculations. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00262-4

    Article  PubMed  Google Scholar 

  86. Nikitin A, Milchevskiy Y, Lyubartsev A (2015) AMBER-Ii: new combining rules and force field for perfluoroalkanes. J Phys Chem B 119(46):14563–14573. https://doi.org/10.1021/acs.jpcb.5b07233

    Article  CAS  PubMed  Google Scholar 

  87. Lyubartsev AP, Martsinovski AA, Shevkunov SV, Vorontsov-Velyaminov PN (1992) New approach to Monte Carlo calculation of the free energy: method of expanded ensembles. J Chem Phys 96(3):1776–1783. https://doi.org/10.1063/1.462133

    Article  CAS  Google Scholar 

  88. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD (2009) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM All-atom additive biological force fields. J Comput Chem. https://doi.org/10.1002/jcc.21367

    Article  Google Scholar 

  89. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. Nonpolar gases. J Chem Phys 22(8):1420–1426. https://doi.org/10.1063/1.1740409

    Article  CAS  Google Scholar 

  90. Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22(2):245–268. https://doi.org/10.1016/0021-9991(76)90078-4

    Article  Google Scholar 

  91. Kirkwood JG (1935) Statistical mechanics of fluid mixtures. J Chem Phys 3(5):300–313. https://doi.org/10.1063/1.1749657

    Article  CAS  Google Scholar 

  92. Procacci P, Cardelli C (2014) Fast switching alchemical transformations in molecular dynamics simulations. J Chem Theory Comput 10(7):2813–2823. https://doi.org/10.1021/ct500142c

    Article  CAS  PubMed  Google Scholar 

  93. Jarzynski C (1997) Nonequilibrium equality for free energy differences. Phys Rev Lett 78(14):2690–2693. https://doi.org/10.1103/PhysRevLett.%78.2690

  94. Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124105. https://doi.org/10.1063/1.2978177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Izadi S, Onufriev AV (2016) Accuracy limit of rigid 3-point water models. J Chem Phys 145(7):074501. https://doi.org/10.1063/1.4960175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Dodda LS, Cabeza de Vaca I, Tirado-Rives J, Jorgensen WL (2017) LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res 45(W1):W331–W336. https://doi.org/10.1093/nar/gkx312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Vassetti D, Pagliai M, Procacci P (2019) Assessment of GAFF2 and OPLS-AA general force fields in combination with the water models TIP3P, SPCE, and OPC3 for the solvation free energy of druglike organic molecules. J Chem Theory Comput 15(3):1983–1995. https://doi.org/10.1021/acs.jctc.8b01039

    Article  CAS  PubMed  Google Scholar 

  98. Bultinck P, Van Alsenoy C, Ayers PW, Carbo-Dorca R (2007) Critical analysis and extension of the Hirshfeld atoms in molecules. J Chem Phys 126(14):144111. https://doi.org/10.1063/1.2715563

    Article  CAS  PubMed  Google Scholar 

  99. Verstraelen T, Vandenbrande S, Heidar-Zadeh F, Vanduyfhuys L, Van Speybroeck V, Waroquier M, Ayers PW (2016) Minimal basis iterative stockholder: atoms in molecules for force-field development. J Chem Theory Comput 12(8):3894–3912. https://doi.org/10.1021/acs.jctc.6b00456

    Article  CAS  PubMed  Google Scholar 

  100. Kusalik PG, Svishchev IM (1994) The spatial structure in liquid water. Science 265(5176):1219–1221. https://doi.org/10.1126/science.265.5176.1219

    Article  CAS  PubMed  Google Scholar 

  101. Li H, Chowdhary J, Huang L, He X, MacKerell AD, Roux B (2017) Drude polarizable force field for molecular dynamics simulations of saturated and unsaturated zwitterionic lipids. J Chem Theory Comput 13(9):4535–4552. https://doi.org/10.1021/acs.jctc.7b00262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Kamath G, Kurnikov I, Fain B, Leontyev I, Illarionov A, Butin O, Olevanov M, Pereyaslavets L (2016) Prediction of cyclohexane-water distribution coefficient for SAMPL5 drug-like compounds with the QMPFF3 and ARROW polarizable force fields. J Comput Aided Mol Des 30(11):977–988. https://doi.org/10.1007/s10822-016-9958-4

    Article  CAS  PubMed  Google Scholar 

  103. Bannan CC, Calabró G, Kyu DY, Mobley DL (2016) Calculating partition coefficients of small molecules in octanol/water and cyclohexane/water. J Chem Theory Comput 12(8):4015–4024. https://doi.org/10.1021/acs.jctc.6b00449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Niemi GJ, Basak SC, Grunwald G, Veith GD (1992) Prediction of octanol/water partition coefficient ( Kow ) with algorithmically derived variables. Environ Toxicol Chem 11(7):893–900. https://doi.org/10.1002/etc.5620110703

    Article  CAS  Google Scholar 

  105. Tetko IV, Tanchuk VY, Villa AEP (2001) Prediction of N-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices. J Chem Inf Comput Sci 41(5):1407–1421. https://doi.org/10.1021/ci010368v

    Article  CAS  PubMed  Google Scholar 

  106. Vraka C, Nics L, Wagner KH, Hacker M, Wadsak W, Mitterhauser M (2017) Log P, a yesterday’s value? Nucl Med Biol 50:1–10. https://doi.org/10.1016/j.nucmedbio.2017.03.003

    Article  CAS  PubMed  Google Scholar 

  107. Glomme A, März J, Dressman JB (2005) Comparison of a miniaturized shake-flask solubility method with automated potentiometric acid/base titrations and calculated solubilities. J Pharm Sci 94(1):1–16. https://doi.org/10.1002/jps.20212

    Article  CAS  PubMed  Google Scholar 

  108. Slater B, McCormack A, Avdeef A, Comer JEA (1994) PH-metric logP.4. Comparison of partition coefficients determined by HPLC and potentiometric methods to literature values. J Pharm Sci 83(9):1280–1283. https://doi.org/10.1002/jps.2600830918

  109. Mobley DL, Bayly CI, Cooper MD, Dill KA (2009) Predictions of hydration free energies from all-atom molecular dynamics simulations. J Phys Chem B 113:4533–4537

    Article  CAS  Google Scholar 

  110. Cournia Z, Allen B, Sherman W (2017) Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J Chem Inf Model 57(12):2911–2937. https://doi.org/10.1021/acs.jcim.7b00564

    Article  CAS  PubMed  Google Scholar 

  111. Case D, Berryman J, Betz R, Cerutti D, Cheatham Iii T, Darden T, Duke R, Giese T, Gohlke H, Goetz A et al (2015) AMBER 2015. University of California, San Francisco

    Google Scholar 

  112. Martínez L, Andrade R, Birgin EG, Martínez JM (2009) PACKMOL: a package for building initial configurations for molecular dynamics simulations. J Comput Chem 30(13):2157–2164. https://doi.org/10.1002/jcc.21224

    Article  CAS  PubMed  Google Scholar 

  113. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang LP, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659. https://doi.org/10.1371/journal.pcbi.1005659

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Hopkins CW, Le Grand S, Walker RC, Roitberg AE (2015) Long-time-step molecular dynamics through hydrogen mass repartitioning. J Chem Theory Comput 11(4):1864–1874. https://doi.org/10.1021/ct5010406

    Article  CAS  PubMed  Google Scholar 

  115. Rizzi A, Chodera J, Naden L, Beauchamp K, Albanese S, Grinaway P, Rustenburg B, Saladi S, Boehm K (2018) choderalab/yank: Bugfix release. Zenodo. https://doi.org/10.5281/zenodo.1447109

    Article  Google Scholar 

  116. Gerber PR (1998) Charge distribution from a simple molecular orbital type calculation and non-bonding interaction terms in the force field MAB. J Comput Aided Mol Des 12(1):37–51. https://doi.org/10.1023/A:1007902804814

    Article  CAS  PubMed  Google Scholar 

  117. Milletti F, Storchi L, Sforna G, Cruciani G (2007) New and original p \({\mathit{K}}_{{\rm a}}\) prediction method using grid molecular interaction fields. J Chem Inf Model 47(6):2172–2181. https://doi.org/10.1021/ci700018y

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank OpenEye, especially Gaetano Calabró, for help with Orion, and for constructing the Orion workflows partially utilized here. We would like to thank experimental collaborators Timothy Rhodes (ORCID: 0000-0001-7534-9221), Dorothy Levorse, and Brad Sherborne (ORCID: 0000-0002-0037-3427). MI and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges partial support from NIH Grant P30 CA008748. MI, TDB, JDC, and DLM gratefully acknowledge support from NIH Grant R01GM124270 supporting the SAMPL Blind challenges. MI acknowledges support from a Doris J. Hutchinson Fellowship during the collection of experimental data. TDB acknowledges support from the ACM SIGHPC/Intel Fellowship. DLM appreciates financial support from the National Institutes of Health (1R01GM108889-01) and the National Science Foundation (CHE 1352608). We acknowledge contributions from Caitlin Bannan who provided feedback on experimental data collection and structure of log P challenge from a computational chemist’s perspective. MI and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. TF thanks BioByte, MOE, and Molecular Discovery for allowing us the include log P predictions calculated by their software in this work as empirical reference calculations.

Disclaimers

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. Mehtap Işık and Teresa Danielle Bergazin have contributed equally to this work.

Authors and Affiliations

  1. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Mehtap Işık, Andrea Rizzi & John D. Chodera

  2. Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA

    Mehtap Işık

  3. Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA

    Teresa Danielle Bergazin & David L. Mobley

  4. Department of Chemistry, University of California, Irvine, CA, 92697, USA

    David L. Mobley

  5. Computational Chemistry, Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach, Germany

    Thomas Fox

  6. Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA

    Andrea Rizzi

Authors
  1. Mehtap Işık
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teresa Danielle Bergazin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrea Rizzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John D. Chodera
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David L. Mobley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, MI, JDC, DLM ; Methodology, MI, TDB, DM, JDC ; Software, MI, TDB, AR ; Formal Analysis, MI, TDB ; Investigation, MI, TDB, DLM, TF; Resources, JDC, DLM; Data Curation, MI, TDB ; Writing-Original Draft, MI, TDB, DLM, TF; Writing - Review and Editing, MI, TDB, DLM, TF, JDC, AZ; Visualization, MI, TDB ; Supervision, DLM, JDC ; Project Administration, MI ; Funding Acquisition, DLM, JDC, MI, TDB.

Corresponding author

Correspondence to Mehtap Işık.

Ethics declarations

Conflict of interest

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software, and DLM is an Open Science Fellow with Silicon Therapeutics. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Vir Biotechnology, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, The Einstein Foundation, and the Sloan Kettering Institute. A complete list of funding can be found at http://choderalab.org/funding.

Electronic supplementary material

Below are the links to the electronic supplementary materials.

Supplementary material 1 (PDF 1633 KB)

Supplementary material 2 (GZ 31113 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Işık, M., Bergazin, T.D., Fox, T. et al. Assessing the accuracy of octanol–water partition coefficient predictions in the SAMPL6 Part II log P Challenge. J Comput Aided Mol Des 34, 335–370 (2020). https://doi.org/10.1007/s10822-020-00295-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-020-00295-0

Keywords

Search

Navigation