Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions among its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early-stage drug discovery. However, many hurdles remain in making them a robust and reliable tool. In this review, we highlight key challenges of these calculations, discuss some examples of these challenges, and call for the designation of standard community benchmark test systems that will help the research community generate and evaluate progress. In our view, progress will require careful assessment and evaluation of new methods, force fields, and modeling innovations on well-characterized benchmark systems, and we lay out our vision for how this can be achieved.


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Literature Cited

  1. Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC. 1.  2016. Accurate calculation of the absolute free energy of binding for drug molecules. Chem. Sci. 7:207–18 [Google Scholar]
  2. Allen MP, Tildesley DJ. 2.  1989. Computer Simulation of Liquids New York: Oxford Univ. Press [Google Scholar]
  3. Assaf KI, Nau WM. 3.  2015. Cucurbiturils: from synthesis to high-affinity binding and catalysis. Chem. Soc. Rev. 44:394–418 [Google Scholar]
  4. Banba S, Brooks CL III. 4.  2000. Free energy screening of small ligands binding to an artificial protein cavity. J. Chem. Phys. 113:3423–33 [Google Scholar]
  5. Banba S, Guo Z, Brooks CL III. 5.  2000. Efficient sampling of ligand orientations and conformations in free energy calculations using the λ-dynamics method. J. Phys. Chem. B 104:6903–10 [Google Scholar]
  6. Bannan C, Burley K, Chiu M, Shirts M, Gilson M, Mobley D. 6.  2016. Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge. J. Comput. Aided Mol. Des. 30:11927–44 [Google Scholar]
  7. Baum B, Muley L, Smolinski M, Heine A, Hangauer D, Klebe G. 7.  2010. Non-additivity of functional group contributions in protein–ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry. J. Mol. Biol. 397:1042–54 [Google Scholar]
  8. Bhakat S, Söderhjelm P. 8.  2016. Resolving the problem of trapped water in binding cavities: prediction of host-guest binding free energies in the SAMPL5 challenge by funnel metadynamics. J. Comput. Aided Mol. Des. 31:1119–32 [Google Scholar]
  9. Boresch S, Tettinger F, Leitgeb M, Karplus M. 9.  2003. Absolute binding free energies: a quantitative approach for their calculation. J. Phys. Chem. B 107:9535–51 [Google Scholar]
  10. Bosisio S, Michel J. 10.  2016. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge. J. Comput. Aided Mol. Des. 31:161–70 [Google Scholar]
  11. Bouvignies G, Vallurupalli P, Hansen D, Correia B, Lange O. 11.  et al. 2011. Solution structure of a minor and transiently formed state of a T4 lysozyme mutant.. Nature 7362:111–14 [Google Scholar]
  12. Boyce SE, Mobley DL, Rocklin GJ, Graves AP, Dill KA, Shoichet BK. 12.  2009. Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site. J. Mol. Biol. 394:747–63 [Google Scholar]
  13. Calabrò G. 13.  2015. Accelerating molecular simulations: implication for rational drug design PhD Thesis, Univ. Edinburgh, Edinburgh, Scotl. [Google Scholar]
  14. Calabrò G, Woods CJ, Powlesland F, Mey ASJS, Mulholland AJ, Michel J. 14.  2016. Elucidation of nonadditive effects in protein–ligand binding energies: thrombin as a case study. J. Phys. Chem. B 120:245340–50 [Google Scholar]
  15. Cao L, Šekutor M, Zavalij PY, Mlinarić-Majerski K, Glaser R, Isaacs L. 15.  2014. Cucurbit[7]uril·guest pair with an attomolar dissociation constant. Angew. Chem. Int. Ed. 53:988–93 [Google Scholar]
  16. Carnegie RS, Gibb CLD, Gibb BC. 16.  2014. Anion complexation and the Hofmeister effect. Angew. Chem. Int. Ed. 126:11682–84 [Google Scholar]
  17. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS. 17.  2011. Alchemical free energy methods for drug discovery: progress and challenges. Curr. Opin. Struct. Biol. 21:150–60 [Google Scholar]
  18. Christ CD. 18.  2016. Binding affinity prediction from molecular simulations: a new standard method in structure-based drug design? Presented at Workshop, Free Energy Methods in Drug Design: Targeting Cancer, May 19–20, Boston [Google Scholar]
  19. Christ CD, Fox T. 19.  2014. Accuracy assessment and automation of free energy calculations for drug design. J. Chem. Inf. Model. 54:108–20 [Google Scholar]
  20. Christ CD, Mark AE, Gunsteren WF. 20.  2010. Basic ingredients of free energy calculations: a review. J. Comput. Chem. 31:1569–82 [Google Scholar]
  21. Cole DJ, Tirado-Rives J, Jorgensen WL. 21.  2015. Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design. Biochim. Biophys. Acta 1850:966–71 [Google Scholar]
  22. Collins M, Quillin M, Hummer G, Matthews B, Gruner S. 22.  2007. Structural rigidity of a large cavity-containing protein revealed by high-pressure crystallography. J. Mol. Biol. 367:752–63 [Google Scholar]
  23. Comer J, Schulten K, Chipot C. 23.  2014. Calculation of lipid-bilayer permeabilities using an average force. J. Chem. Theory Comput. 10:554–64 [Google Scholar]
  24. Cong H, Ni XL, Xiao X, Huang Y, Zhu QJ. 24.  et al. 2016. Synthesis and separation of cucurbit[n]urils and their derivatives. Org. Biomol. Chem. 14:4335–64 [Google Scholar]
  25. Cui G. 25.  2016. Affinity predictions with FEP+: a different perspective on performance and utility Presented at Workshop, Free Energy Methods in Drug Design: Targeting Cancer, May 19–20, Boston [Google Scholar]
  26. de Ruiter A, Oostenbrink C. 26.  2013. Protein–ligand binding from distancefield distances and Hamiltonian replica exchange simulations. J. Chem. Theory Comput. 9:883–92 [Google Scholar]
  27. Deng N, Forli S, He P, Perryman A, Wickstrom L. 27.  et al. 2015. Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease. J. Phys. Chem. B 119:976–88 [Google Scholar]
  28. Deng Y, Roux B. 28.  2006. Calculation of standard binding free energies: aromatic molecules in the T4 lysozyme L99A mutant. J. Chem. Theory Comput. 2:1255–73 [Google Scholar]
  29. Dunbar JB, Smith RD, Yang CY, Ung PMU, Lexa KW. 29.  et al. 2011. CSAR benchmark exercise of 2010: selection of the protein–ligand complexes. J. Chem. Inf. Model. 51:2036–46 [Google Scholar]
  30. Eklund A, Nichols TE, Knutsson H. 30.  2016. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. PNAS 113:7900–5 [Google Scholar]
  31. Eriksson AE, Baase WA, Wozniak JA, Matthews BW. 31.  1992. A cavity-containing mutant of T4 lysozyme is stabilized by buried benzene. Nature 355:371–73 [Google Scholar]
  32. Ewell J, Gibb BC, Rick SW. 32.  2008. Water inside a hydrophobic cavitand molecule. J. Phys. Chem. B 112:10272–79 [Google Scholar]
  33. Fenley AT, Henriksen NM, Muddana HS, Gilson MK. 33.  2014. Bridging calorimetry and simulation through precise calculations of cucurbituril–guest binding enthalpies. J. Chem. Theory Comput. 10:4069–78 [Google Scholar]
  34. Fitzgerald MM, Musah RA, McRee DE, Goodin DB. 34.  1996. A ligand-gated, hinged loop rearrangement opens a channel to a buried artificial protein cavity. Nat. Struct. Mol. Biol. 3:626–31 [Google Scholar]
  35. Flyvbjerg H, Petersen HG. 35.  1989. Error estimates on averages of correlated data. J. Chem. Phys. 91:461–66 [Google Scholar]
  36. Freeman WA, Mock WL, Shih NY. 36.  1981. Cucurbituril. J. Am. Chem. Soc. 103:7367–68 [Google Scholar]
  37. Gallicchio E, Lapelosa M, Levy RM. 37.  2010. Binding energy distribution analysis method (BEDAM) for estimation of protein-ligand binding affinities. J. Chem. Theory Comput. 6:2961–77 [Google Scholar]
  38. Gan H, Benjamin CJ, Gibb BC. 38.  2011. Nonmonotonic assembly of a deep-cavity cavitand. J. Am. Chem. Soc. 133:4770–73 [Google Scholar]
  39. Gao K, Yin J, Henriksen NM, Fenley AT, Gilson MK. 39.  2015. Binding enthalpy calculations for a neutral host–guest pair yield widely divergent salt effects across water models. J. Chem. Theory Comput. 11:4555–64 [Google Scholar]
  40. Gathiaka S, Liu S, Chiu M, Yang H, Stuckey J. 40.  et al. 2016. D3R Grand Challenge 2015: evaluation of protein-ligand pose and affinity predictions. J. Comput. Aided Mol. Des. 30:651–68 [Google Scholar]
  41. Gibb CLD, Gibb BC. 41.  2004. Well-defined, organic nanoenvironments in water: The hydrophobic effect drives a capsular assembly. J. Am. Chem. Soc. 126:11408–9 [Google Scholar]
  42. Gibb CLD, Gibb BC. 42.  2009. Guests of differing polarities provide insight into structural requirements for templates of water-soluble nano-capsules. Tetrahedron 65:7240–48 [Google Scholar]
  43. Gibb CLD, Gibb BC. 43.  2011. Anion binding to hydrophobic concavity is central to the salting-in effects of Hofmeister chaotropes. J. Am. Chem. Soc. 133:7344–47 [Google Scholar]
  44. Gibb CLD, Gibb BC. 44.  2013. Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J. Comput. Aided Mol. Des. 28:319–25 [Google Scholar]
  45. Gilson MK, Given JA, Bush BL, McCammon JA. 45.  1997. The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys. J. 72:1047–69 [Google Scholar]
  46. Godínez LA, Schwartz L, Criss CM, Kaifer AE. 46.  1997. Thermodynamic studies on the cyclodextrin complexation of aromatic and aliphatic guests in water and water-urea mixtures. Experimental evidence for the interaction of urea with arene surfaces. J. Phys. Chem. B 101:3376–80 [Google Scholar]
  47. Graves AP, Brenk R, Shoichet BK. 47.  2005. Decoys for docking. J. Med. Chem. 48:3714–28 [Google Scholar]
  48. Graves AP, Shivakumar DM, Boyce SE, Jacobson MP, Case DA, Shoichet BK. 48.  2008. Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J. Mol. Biol. 377:914–34 [Google Scholar]
  49. Gumbart JC, Roux B, Chipot C. 49.  2013. Standard binding free energies from computer simulations: What is the best strategy. J. Chem. Theory Comput. 9:794–802 [Google Scholar]
  50. Henriksen NM, Fenley AT, Gilson MK. 50.  2015. Computational calorimetry: high-precision calculation of host–guest binding thermodynamics. J. Chem. Theory Comput. 11:4377–94 [Google Scholar]
  51. Hermans J, Subramaniam S. 51.  1986. The free energy of xenon binding to myoglobin from molecular dynamics simulation. Isr. J. Chem. 27:225–27 [Google Scholar]
  52. Hillyer MB, Gibb CLD, Sokkalingam P, Jordan JH, Ioup SE, Gibb BC. 52.  2016. Synthesis of water-soluble deep-cavity cavitands. Org. Lett. 18:4048–51 [Google Scholar]
  53. Homeyer N, Stoll F, Hillisch A, Gohlke H. 53.  2014. Binding free energy calculations for lead optimization: assessment of their accuracy in an industrial drug design context. J. Chem. Theory Comput. 10:3331–44 [Google Scholar]
  54. Hsiao YW, Söderhjelm P. 54.  2014. Prediction of SAMPL4 host–guest binding affinities using funnel metadynamics. J. Comput. Aided Mol. Des. 28:443–54 [Google Scholar]
  55. Jiang W, Roux B. 55.  2010. Free energy perturbation Hamiltonian replica-exchange molecular dynamics (FEP/H-REMD) for absolute ligand binding free energy calculations. J. Chem. Theory Comput. 6:2559–65 [Google Scholar]
  56. Jorgensen WL. 56.  1981. Quantum and statistical mechanical studies of liquids. 12. Simulation of liquid ethanol including internal rotation. J. Am. Chem. Soc. 103:345–50 [Google Scholar]
  57. Jorgensen WL, Buckner JK, Boudon S, Rives J. 57.  1988. Efficient computation of absolute free energies of binding by computer simulations. Application to the methane dimer in water. J. Chem. Phys. 89:3742–46 [Google Scholar]
  58. Karplus M, McCammon JA. 58.  2002. Molecular dynamics simulations of biomolecules. Nat. Struct. Mol. Biol. 9:646–52 [Google Scholar]
  59. Khavrutskii IV, Wallqvist A. 59.  2011. Improved binding free energy predictions from single-reference thermodynamic integration augmented with Hamiltonian replica exchange. J. Chem. Theory Comput. 7:3001–11 [Google Scholar]
  60. Kitahara R, Mulder F. 60.  2015. Is pressure-induced signal loss in NMR spectra for the Leu99Ala cavity mutant of T4 lysozyme due to unfolding. PNAS 112:E923 [Google Scholar]
  61. Lee CT, Comer J, Herndon C, Leung N, Pavlova A. 61.  et al. 2016. Simulation-based approaches for determining membrane permeability of small compounds. J. Chem. Inf. Model. 56:721–33 [Google Scholar]
  62. Lee JW, Lee HHL, Ko YH, Kim K, Kim HI. 62.  2015. Deciphering the specific high-affinity binding of cucurbit[7]uril to amino acids in water. J. Phys. Chem. B 119:4628–36 [Google Scholar]
  63. Lee JW, Samal S, Selvapalam N, Kim HJ, Kim K. 63.  2003. Cucurbituril homologues and derivatives: new opportunities in supramolecular chemistry. Acc. Chem. Res. 36:621–30 [Google Scholar]
  64. Lee MS, Olson MA. 64.  2006. Calculation of absolute protein-ligand binding affinity using path and endpoint approaches. Biophys. J. 90:864–77 [Google Scholar]
  65. Leonis G, Steinbrecher T, Papadopoulos MG. 65.  2013. A contribution to the drug resistance mechanism of darunavir, amprenavir, indinavir, and saquinavir complexes with HIV-1 protease due to flap mutation I50V: a systematic MM–PBSA and thermodynamic integration study. J. Chem. Inf. Model. 53:2141–53 [Google Scholar]
  66. Lerch M, Lopez C, Yang Z, Kreitman M, Horwitz J, Hubbell W. 66.  2015. Structure-relaxation mechanism for the response of T4 lysozyme cavity mutants to hydrostatic pressure. PNAS 112:E2437–46 [Google Scholar]
  67. Lim NM, Wang L, Abel R, Mobley DL. 67.  2016. Sensitivity in binding free energies due to protein reorganization. J. Chem. Theory Comput. 12:94620–31 [Google Scholar]
  68. Lin YL, Aleksandrov A, Simonson T, Roux B. 68.  2014. An overview of electrostatic free energy computations for solutions and proteins. J. Chem. Theory Comput. 10:2690–709 [Google Scholar]
  69. Liu S, Cao S, Hoang K, Young KL, Paluch AS, Mobley DL. 69.  2016. Using MD simulations to calculate how solvents modulate solubility. J. Chem. Theory Comput. 12:1930–41 [Google Scholar]
  70. Liu S, Ruspic C, Mukhopadhyay P, Chakrabarti S, Zavalij PY, Isaacs L. 70.  2005. The cucurbit[n]uril family: prime components for self-sorting systems. J. Am. Chem. Soc. 127:15959–67 [Google Scholar]
  71. Liu S, Wu Y, Lin T, Abel R, Redmann JP. 71.  et al. 2013. Lead optimization mapper: automating free energy calculations for lead optimization. J. Comput. Aided Mol. Des. 27:755–70 [Google Scholar]
  72. Maeno A, Sindhikara D, Hirata F, Otten R, Dahlquist F. 72.  et al. 2015. Cavity as a source of conformational fluctuation and high-energy state: high-pressure NMR study of a cavity-enlarged mutant of T4 lysozyme. Biophys. J. 108:133–45 [Google Scholar]
  73. Merski M, Fischer M, Balius TE, Eidam O, Shoichet BK. 73.  2015. Homologous ligands accommodated by discrete conformations of a buried cavity. PNAS 112:5039–44 [Google Scholar]
  74. Michel J, Essex JW. 74.  2008. Hit identification and binding mode predictions by rigorous free energy simulations. J. Med. Chem. 51:6654–64 [Google Scholar]
  75. Michel J, Essex JW. 75.  2010. Prediction of protein–ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. J. Comput. Aided Mol. Des. 24:639–58 [Google Scholar]
  76. Mikulskis P, Cioloboc D, Andrejić M, Khare S, Brorsson J. 76.  et al. 2014. Free-energy perturbation and quantum mechanical study of SAMPL4 octa-acid host–guest binding energies. J. Comput. Aided Mol. Des. 28:375–400 [Google Scholar]
  77. Mikulskis P, Genheden S, Ryde U. 77.  2014. A large-scale test of free-energy simulation estimates of protein–ligand binding affinities. J. Chem. Inf. Model. 54:2794–806 [Google Scholar]
  78. Mobley DL, Chodera JD, Dill KA. 78.  2006. On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J. Chem. Phys. 125:084902 [Google Scholar]
  79. Mobley DL, Chodera JD, Dill KA. 79.  2007. Confine-and-release method: obtaining correct binding free energies in the presence of protein conformational change. J. Chem. Theory Comput. 3:1231–35 [Google Scholar]
  80. Mobley DL, Chodera J, Isaacs L, Gibb B. 80.  2016. Advancing predictive modeling through focused development of new systems to drive new modeling innovations: a funding proposal recently submitted to the NIH Funding Propos., Natl. Inst. Health, Bethesda, MD. https://doi.org/10.5281/zenodo.163963 [Crossref] [Google Scholar]
  81. Mobley DL, Graves AP, Chodera JD, McReynolds AC, Shoichet BK, Dill KA. 81.  2007. Predicting absolute ligand binding free energies to a simple model site. J. Mol. Biol. 371:1118–34 [Google Scholar]
  82. Mobley DL, Klimovich PV. 82.  2012. Perspective: alchemical free energy calculations for drug discovery. J. Chem. Phys. 137:230901 [Google Scholar]
  83. Mobley DL, Zuckerman DM. 83.  2015. A proposal for regularly updated review/survey articles: “perpetual reviews.”. arXiv:1502.01329 [cs.DL]
  84. Mock WL, Shih NY. 84.  1983. Host-guest binding capacity of cucurbituril. J. Org. Chem. 48:3618–19 [Google Scholar]
  85. Moghaddam S, Inoue Y, Gilson MK. 85.  2009. Host-guest complexes with protein-ligand-like affinities: computational analysis and design. J. Am. Chem. Soc. 131:4012–21 [Google Scholar]
  86. Moghaddam S, Yang C, Rekharsky M, Ko YH, Kim K. 86.  et al. 2011. New ultrahigh affinity host-guest complexes of cucurbit[7]uril with bicyclo[2.2.2]octane and adamantane guests: thermodynamic analysis and evaluation of M2 affinity calculations. J. Am. Chem. Soc. 133:3570–81 [Google Scholar]
  87. Monroe JI, Shirts MR. 87.  2014. Converging free energies of binding in cucurbit[7]uril and octa-acid host–guest systems from SAMPL4 using expanded ensemble simulations. J. Comput. Aided Mol. Des. 28:401–15 [Google Scholar]
  88. Morton A, Baase WA, Matthews BW. 88.  1995. Energetic origins of specificity of ligand binding in an interior nonpolar cavity of T4 lysozyme. Biochemistry 34:8564–75 [Google Scholar]
  89. Morton A, Matthews BW. 89.  1995. Specificity of ligand binding in a buried nonpolar cavity of T4 lysozyme: linkage of dynamics and structural plasticity. Biochemistry 34:8576–88 [Google Scholar]
  90. Muddana HS, Fenley AT, Mobley DL, Gilson MK. 90.  2014. The SAMPL4 host–guest blind prediction challenge: an overview. J. Comput. Aided Mol. Des. 28:305–17 [Google Scholar]
  91. Muddana HS, Gilson MK. 91.  2012. Prediction of SAMPL3 host–guest binding affinities: evaluating the accuracy of generalized force-fields. J. Comput. Aided Mol. Des. 26:517–25 [Google Scholar]
  92. Muddana HS, Varnado CD, Bielawski CW, Urbach AR, Isaacs L. 92.  et al. 2012. Blind prediction of host–guest binding affinities: a new SAMPL3 challenge. J. Comput. Aided Mol. Des. 26:475–87 [Google Scholar]
  93. Muddana HS, Yin J, Sapra NV, Fenley AT, Gilson MK. 93.  2014. Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method. J. Comput. Aided Mol. Des. 28:463–74 [Google Scholar]
  94. Musah RA, Jensen GM, Bunte SW, Rosenfeld RJ, Goodin DB. 94.  2002. Artificial protein cavities as specific ligand-binding templates: characterization of an engineered heterocyclic cation-binding site that preserves the evolved specificity of the parent protein. J. Mol. Biol. 315:845–57 [Google Scholar]
  95. Nguyen CN, Young TK, Gilson MK. 95.  2012. Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. J. Chem. Phys. 137:044101 [Google Scholar]
  96. Nucci N, Fuglestad B, Athanasoula E, Wand A. 96.  2014. Role of cavities and hydration in the pressure unfolding of T4 lysozyme. PNAS 111:13846–51 [Google Scholar]
  97. Pal RK, Haider K, Kaur D, Flynn W, Xia J. 97.  et al. 2016. A combined treatment of hydration and dynamical effects for the modeling of host-guest binding thermodynamics: the SAMPL5 blinded challenge. J. Comput. Aided Mol. Des. 31:129–44 [Google Scholar]
  98. Park J, Nessler I, McClain B, Macikenas D, Baltrusaitis J, Schnieders MJ. 98.  2014. Absolute organic crystal thermodynamics: growth of the asymmetric unit into a crystal via alchemy. J. Chem. Theory Comput. 10:2781–91 [Google Scholar]
  99. Reif MM, Oostenbrink C. 99.  2014. Net charge changes in the calculation of relative ligand-binding free energies via classical atomistic molecular dynamics simulation. J. Comput. Chem. 35:227–43 [Google Scholar]
  100. Rekharsky MV, Inoue Y. 100.  1998. Complexation thermodynamics of cyclodextrins. Chem. Rev. 98:1875–918 [Google Scholar]
  101. Rekharsky MV, Mori T, Yang C, Ko YH, Selvapalam N. 101.  et al. 2007. A synthetic host-guest system achieves avidin-biotin affinity by overcoming enthalpy–entropy compensation. PNAS 104:20737–42 [Google Scholar]
  102. Rocklin GJ, Boyce SE, Fischer M, Fish I, Mobley DL. 102.  et al. 2013. Blind prediction of charged ligand binding affinities in a model binding site. J. Mol. Biol. 425:4569–83 [Google Scholar]
  103. Rocklin GJ, Mobley DL, Dill KA, Hünenberger PH. 103.  2013. Calculating the binding free energies of charged species based on explicit-solvent simulations employing lattice-sum methods: an accurate correction scheme for electrostatic finite-size effects. J. Chem. Phys. 139:184103 [Google Scholar]
  104. Rogers KE, McCammon JA. 104.  2013. On the role of dewetting transitions in host–guest binding free energy calculations. J. Chem. Theory Comput. 9:46–53 [Google Scholar]
  105. Rosenfeld RJ, Hays AMA, Musah RA, Goodin DB. 105.  2002. Excision of a proposed electron transfer pathway in cytochrome c peroxidase and its replacement by a ligand-binding channel. Protein Sci. 11:1251–59 [Google Scholar]
  106. Schnieders MJ, Baltrusaitis J, Shi Y, Chattree G, Zheng L. 106.  et al. 2012. The structure, thermodynamics, and solubility of organic crystals from simulation with a polarizable force field. J. Chem. Theory Comput. 8:1721–36 [Google Scholar]
  107. Schreiner P. 107.  2016. Theoretical prediction of affinities to cucurbiturils—the blind prediction HYDROPHOBE Challenge Justus-Liebig Univ. Giessen, Giessen, Ger. https://www.uni-giessen.de/fbz/fb08/dispersion/projects/HydrophobeChallenge [Google Scholar]
  108. Sherborne B, Shanmugasundaram V, Cheng AC, Christ CD, DesJarlais RL. 108.  et al. 2016. Collaborating to improve the use of free-energy and other quantitative methods in drug discovery. J. Comput. Aided Mol. Des. 30:121139–41 [Google Scholar]
  109. Shirts MR, Chodera JD. 109.  2008. Statistically optimal analysis of samples from multiple equilibrium states. J. Chem. Phys. 129:124105 [Google Scholar]
  110. Shirts MR, Klein C, Swails JM, Yin J, Gilson MK. 110.  et al. 2016. Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset. J. Comput. Aided Mol. Des. 31:147–61 [Google Scholar]
  111. Shirts MR, Mobley DL. 111.  2013. An introduction to best practices in free energy calculations. Biomolecular Simulations L Monticelli, E Salonen 271–311 New York: Springer [Google Scholar]
  112. Shirts MR, Mobley DL, Brown SP. 112.  2010. Free-energy calculations in structure-based drug design. Drug Design: Structure and Ligand-Based Approaches KM Merz Jr., D Ringe, CH Reynolds 61–86 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  113. Simonson T, Roux B. 113.  2016. Concepts and protocols for electrostatic free energies. Mol. Simul. 42:1090–101 [Google Scholar]
  114. Sokkalingam P, Shraberg J, Rick SW, Gibb BC. 114.  2016. Binding hydrated anions with hydrophobic pockets. J. Am. Chem. Soc. 138:48–51 [Google Scholar]
  115. Sullivan MR, Sokkalingam P, Nguyen T, Donahue JP, Gibb BC. 115.  2016. Binding of carboxylate and trimethylammonium salts to octa-acid and TEMOA deep-cavity cavitands. J. Comput. Aided Mol. Des. 31:21–28 [Google Scholar]
  116. Sun H, Gibb CLD, Gibb BC. 116.  2008. Calorimetric analysis of the 1:1 complexes formed between a water-soluble deep-cavity cavitand, and cyclic and acyclic carboxylic acids. Supramol. Chem. 20:141–47 [Google Scholar]
  117. Tai K. 117.  2004. Conformational sampling for the impatient. Biophys. Chem. 107:213–20 [Google Scholar]
  118. Tembe BL, McCammon JA. 118.  1984. Ligand receptor interactions. Comput. Chem. 8:281–83 [Google Scholar]
  119. Tofoleanu F, Lee J, Pickard FC 4th, König G, Huang J. 119.  et al. 2017. Absolute binding free energies for octa acids and guests in SAMPL5. J. Comput. Aided Mol. Des. 31:107–18 [Google Scholar]
  120. Velez-Vega C, Gilson MK. 120.  2012. Force and stress along simulated dissociation pathways of cucurbituril–guest systems. J. Chem. Theory Comput. 8:966–76 [Google Scholar]
  121. Velez-Vega C, Gilson MK. 121.  2013. Overcoming dissipation in the calculation of standard binding free energies by ligand extraction. J. Comput. Chem. 34:2360–71 [Google Scholar]
  122. Verras A. 122.  2016. Free energy perturbation at Merck: benchmarking against faster methods Presented at Workshop, Free Energy Methods in Drug Design: Targeting Cancer, May 19–20, Boston, MA. http://www.alchemistry.org/wiki/images/c/c3/Vertex_FreeEnergyWorkshop2016_AV.pdf [Google Scholar]
  123. Vinciguerra B, Zavalij PY, Isaacs L. 123.  2015. Synthesis and recognition properties of cucurbit[8]uril derivatives. Org. Lett. 17:5068–71 [Google Scholar]
  124. Wand A, Nucci N. 124.  2015. Reply to Kitahara and Mulder: An ensemble view of protein stability best explains pressure effects in a T4 lysozyme cavity mutant. PNAS 112:E924 [Google Scholar]
  125. Wang K, Chodera JD, Yang Y, Shirts MR. 125.  2013. Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics. J. Comput. Aided Mol. Des. 27:989–1007 [Google Scholar]
  126. Wang K, Sokkalingam P, Gibb BC. 126.  2016. ITC and NMR analysis of the encapsulation of fatty acids within a water-soluble cavitand and its dimeric capsule. Supramol. Chem. 28:84–90 [Google Scholar]
  127. Wang L, Berne BJ, Friesner RA. 127.  2012. On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities. PNAS 109:1937–42 [Google Scholar]
  128. Wang L, Wu Y, Deng Y, Kim B, Pierce L. 128.  et al. 2015. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc. 137:2695–703 [Google Scholar]
  129. Wei BQ, Baase WA, Weaver LH, Matthews BW, Shoichet BK. 129.  2002. A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 322:339–55 [Google Scholar]
  130. Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK. 130.  2004. Testing a flexible-receptor docking algorithm in a model binding site. J. Mol. Biol. 337:1161–82 [Google Scholar]
  131. Wickstrom L, Deng N, He P, Mentes A, Nguyen C. 131.  et al. 2016. Parameterization of an effective potential for protein–ligand binding from host–guest affinity data. J. Mol. Recognit. 29:10–21 [Google Scholar]
  132. Woo HJ, Roux B. 132.  2005. Calculation of absolute protein–ligand binding free energy from computer simulations. PNAS 102:6825–30 [Google Scholar]
  133. Wyman IW, Macartney DH. 133.  2008. Cucurbit[7]uril host–guest complexes with small polar organic guests in aqueous solution. Org. Biomol. Chem. 6:1796–801 [Google Scholar]
  134. Yin J, Fenley AT, Henriksen NM, Gilson MK. 134.  2015. Toward improved force-field accuracy through sensitivity analysis of host-guest binding thermodynamics. J. Phys. Chem. B 119:10145–55 [Google Scholar]
  135. Yin J, Henriksen NM, Slochower DR, Chiu MW, Mobley DL, Gilson MK. 135.  2016. Overview of the SAMPL5 host-guest challenge: Are we doing better. J. Comput. Aided Mol. Des. 31:1–19 [Google Scholar]
  136. Yin J, Henriksen NM, Slochower DR, Gilson MK. 136.  2016. The SAMPL5 host-guest challenge: computing binding free energies and enthalpies from explicit solvent simulations by the attach-pull-release (APR) method. J. Comput. Aided Mol. Des. 31:133–45 [Google Scholar]
  137. Ytreberg FM. 137.  2009. Absolute FKBP binding affinities obtained via nonequilibrium unbinding simulations. J. Chem. Phys. 130:164906 [Google Scholar]

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