American Journal of Modeling and Optimization
ISSN (Print): 2333-1143 ISSN (Online): 2333-1267 Website: https://www.sciepub.com/journal/ajmo Editor-in-chief: Dr Anil Kumar Gupta
Open Access
Journal Browser
Go
American Journal of Modeling and Optimization. 2016, 4(3), 74-114
DOI: 10.12691/ajmo-4-3-2
Open AccessReview Article

Docking and Ligand Binding Affinity: Uses and Pitfalls

María J. R. Yunta1,

1Departamento de Química Orgánica I, Facultad de Química, Universidad Complutense, 28040 Madrid, Spain

Pub. Date: December 01, 2016

Cite this paper:
María J. R. Yunta. Docking and Ligand Binding Affinity: Uses and Pitfalls. American Journal of Modeling and Optimization. 2016; 4(3):74-114. doi: 10.12691/ajmo-4-3-2

Abstract

In this review article, we will explore the foundations of different classes of docking and scoring functions, their possible limitations, and their suitable application domains. We also provide assessments of several scoring functions on weakly-interacting protein-ligand complexes, which will be useful information in computational fragment-based drug design or virtual screening.

Keywords:
molecular modeling docking binding affinity drug scoring computer aided drug design

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Vindigni, A. Energetic dissection of specificity in serine proteases. Comb. Chem. High Throughput Screen, 1999, 2, 139-153.
 
[2]  Cheng, A. C.; Calabro, V.; Frankel, A. D. Design of RNA binding proteins and ligands. Curr. Opin. Struct. Biol., 2001, 11, 478-484.
 
[3]  Garvie, C. W.; Wolberger, C. Recognition of specific DNA sequences. Mol. Cell., 2001, 8, 937-946.
 
[4]  Autumn, K.; Sitti, M.; Liang, Y. A.; Peattie, A. M.; Kenny, T. W.; Fearing, R.; Israelachvili, J. N.; Hansen, W. R.; Sponberg, S.; Full, R. J. Evidence for van der Waals adhesion in gecko setae. Proc. Natl. Acad. Sci. U. S. A., 2002, 99, 12252-12256.
 
[5]  Riley, K. E.; Hobza, P. On the importance and origin of aromatic interactions in chemistry and biodisciplines. Acc. Chem. Res., 2013, 46, 927-936.
 
[6]  Grimme, S. Supramolecular binding thermodynamics by dispersion-corrected density functional theory. Chem. - Eur. J., 2012, 18, 9955-9964.
 
[7]  Juríček, M.; Strutt, N. L.; Barnes, J. C.; Butterfield, A. M.; Dale, E. J.; Baldridge, K. K.; Stoddart, J. F.; Siegel, J. S. Induced-fit catalysis of corannulene bowl-to-bowl inversion. Nat. Chem,. 2014, 6, 222-228.
 
[8]  Liu, W.; Tkatchenko, A.; Scheffler, M. Modeling adsorption and reactions of organic molecules at metal surfaces. Acc. Chem. Res., 2014, 47, 3369-3377.
 
[9]  Kronik, L.; Tkatchenko, A. Understanding molecular crystals with dispersion-inclusive density functional theory: pairwise corrections and beyond. Acc. Chem. Res., 2014, 47, 3208-3216.
 
[10]  Šponer, J.; Šponer, J. E.; Mládek, A.; Jurečka, P.; Banáš, P.; Otyepka, M. Nature and magnitude of aromatic base stacking in DNA and RNA: quantum chemistry, molecular mechanics and experiment. Biopolymers, 2013, 99, 978.
 
[11]  Riley, K. E.; Hobza, P. Noncovalent interactions in biochemistry. Comp. Mol. Sci., 2011, 1, 3-17.
 
[12]  Murthy, P. S. Molecular handshake: recognition through weak noncovalent interactions. J. Chem. Educ., 2006, 83, 1010.
 
[13]  Rubbiani, R.; Can, S.; Kitanovic, I.; Alborzinia, H.; Stefanopoulou, M.; Kokoschka, M.; Mönchgesang, S.; Sheldrick, W. S.; Wölfl, S.; Ott, I. Comparative in vitro evaluation of N-heterocyclic carbene gold(I) complexes of the benzimidazolylidene type. J. Med. Chem., 2011, 54, 8646-8657.
 
[14]  Meyer, A.; Bagowski, C. P.; Kokoschka, M.; Stefanopoulou, M.; Alborzinia, H.; Can, S.; Vlecken, D. H.; Sheldrick, W. S.; Wölfl, S.; Ott, I. On the biological properties of alkynyl phosphine gold(I) complexes. Angew. Chem., Int. Ed., 2012, 51, 8895-8899.
 
[15]  Beno, B. R.; Yeung, K.-S.; Bartberger, M. D.; Pennington, L. D.; Meanwell, N. A. A Survey of the role of noncovalent sulfur interactions in drug design. J. Med. Chem., 2015, 58, 4383-4438.
 
[16]  Riley, K. E.; Pitoňák, M.; Jurečka, P.; Hobza, P. Stabilization and structure calculations for noncovalent interactions in extended molecular systems based on wave function and density functional theories. Chem. Rev., 2010, 110, 5023-5063.
 
[17]  Georgakilas, V.; Otyepka, M.; Bourlinos, A. B.; Chandra, V.; Kim, N.; Kemp, K. C.; Hobza, P.; Zbořil, R.; Kim, K. S. Functionalization of graphene: covalent and non-covalent approaches, derivatives and applications. Chem. Rev., 2012, 112, 6156-6214.
 
[18]  DiStasio, R. A.; Gobre, V. V.; Tkatchenko, A. Many-body van der Waals interactions in molecules and condensed matter. J. Phys.: Condens. Matter, 2014, 26, 213202.
 
[19]  Schneider, G.; Bohm, H. J. Virtual screening and fast automated docking methods. Drug Discov. Today., 2002, 7, 64-70.
 
[20]  Shoichet, B. K.; Leach, A. R.; Kuntz, I. D. Ligand solvation in molecular docking. Proteins., 1999, 34, 4-16.
 
[21]  Chothia, C. Hydrophobic bonding and accessible surface area in proteins. Nature, 1974, 248, 338-9.
 
[22]  Wang, J. M.; Morin, P.; Wang, W.; Kollman, P. A. Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of tibo derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J. Am. Chem. Soc., 2001, 123, 5221-5230.
 
[23]  Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem., Int. Ed., 2002, 41, 2645-2676.
 
[24]  Homans, S. W. Water, water everywhere-except where it matters? Drug Discovery today, 2007, 12, 534-539.
 
[25]  Jeffrey, G.; Saenger, W. Hydrogen bonding in biological structures. 1991, Springer, Berlin.
 
[26]  Stahl, M.; Bohm, H. J. Development of filter functions for protein-ligand docking. J. Mol. Graph. Mod., 1998, 16, 121-132.
 
[27]  Brady, G. P.; Sharp, K. A. Entropy in protein folding and in protein-protein interactions. Curr. Opin. Struct. Biol ., 1997, 7, 215-21.
 
[28]  Page, M. I.; Jencks, W. P. Entropic contributions to rate accelerations in enzymatic and intramolecular reactions and the chelate effect. Proc. Natl. Acad. Sci. USA, 1971, 68, 1678-83.
 
[29]  Boehr, D. D.; Nussinov, R.; Wright, P. E. The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol., 2009, 5, 789-796.
 
[30]  Risthaus, T.; Grimme, S. Benchmarking of london dispersion-accounting density functional theory methods on very large molecular complexes. J. Chem. Theory Comput., 2013, 9, 1580-1591.
 
[31]  Sure, R.; Grimme, S. Comprehensive benchmark of association (free) energies of realistic host-guest complexes. J. Chem. Theory Comput., 2015, 11, 3785-3801.
 
[32]  Cao, L.; Škalamera, D.; Zavalij, P. Y.; Hostaš, J.; Hobza, P.; Mlinarič-Majerski, K.; Glaser, R.; Isaacs, L. Influence of hydrophobic residues on the binding of CB[7] toward diammonium ions of common ammonium·ammonium distance. Org. Biomol. Chem., 2015, 13, 6249-6254.
 
[33]  Lee, J.; Sorescu, D. C.; Deng, X.; Jordan, K. D. Water chain formation on TiO2 rutile(110). J. Phys. Chem. Lett., 2013, 4, 53-57.
 
[34]  Bartels, L. Tailoring molecular layers at metal surfaces. Nat. Chem., 2010, 2, 87-95.
 
[35]  Auwärter, W.; Écija, D.; Klappenberger, F.; Barth, J. V. Porphyrins at interfaces. Nat. Chem., 2015, 7, 105-120.
 
[36]  Vítek, A.; Ofiala, A.; Kalus, R. Thermodynamics of water clusters under high pressures. A case study for (H2O)15 and (H2O)15CH4. Phys. Chem. Chem. Phys., 2012, 14, 15509-15519.
 
[37]  Chattoraj, J.; Risthaus, T.; Rubner, O.; Heuer, A.; Grimme, S. A multi-scale approach to characterize pure CH4, CF4, and CH4/CF4 mixtures. J. Chem. Phys., 2015, 142, 164508.
 
[38]  Řezáč, J.; Fanfrlík, J.; Salahub, D.; Hobza, P. Semiempirical quantum chemical PM6 method augmented by dispersion and h-bonding correction terms reliably describes various types of noncovalent complexes. J. Chem. Theory Comput., 2009, 5, 1749-1760.
 
[39]  Brandenburg, J. G.; Hochheim, M.; Bredow, T.; Grimme, S. Low-cost quantum chemical methods for noncovalent interactions. J. Phys. Chem. Lett., 2014, 5, 4275-4284.
 
[40]  Grimme, S.; Brandenburg, J. G.; Bannwarth, C.; Hansen, A. Consistent structures and interactions by density functional theory with small atomic orbital basis sets. J. Chem. Phys., 2015, 143, 054107.
 
[41]  Grimme, S. Density functional theory with London dispersion corrections. WIREs, 2011, 1, 211-228.
 
[42]  Waller, M. P.; Kruse, H.; Muck-Lichtenfeld, C.; Grimme, S. Investigating inclusion complexes using quantum chemical methods. Chem. Soc. Rev., 2012, 41, 3119-3128.
 
[43]  Chałasiński, G.; Szczeşńiak,M.M. State of the art and challenges of the ab initio theory of intermolecular interactions. Chem. Rev., 2000, 100, 4227-4252.
 
[44]  Pykal, M.; Jurečka, P.; Karlický, F.; Otyepka, M. Modelling of graphene functionalization. Phys. Chem. Chem. Phys., 2016, 18, 6351-6372.
 
[45]  Hobza, P.; Müller-Dethlefs, K. Non-covalent interactions; RSC Theoretical and Computational Chemistry Series; RSC Publishing, 2010.
 
[46]  Lazar, P.; Karlický, F.; Jurečka, P.; Kocman, M.; Otyepková, E.; Šafářová, K.; Otyepka, M. Adsorption of small organic molecules on graphene. J. Am. Chem. Soc., 2013, 135, 6372-6377.
 
[47]  Karlický, F.; Otyepková, E.; Banáš, P.; Lazar, P.; Kocman, M.; Otyepka, M. Interplay between ethanol adsorption to high-energy sites and clustering on graphene and graphite alters the measured isosteric adsorption enthalpies. J. Phys. Chem. C, 2015, 119, 20535-20543.
 
[48]  Stone, A. J. The theory of intermolecular forces. Calerdon Press, Oxford, 2002.
 
[49]  Jeziorski, B.; Moszynski, R.; Szalewicz, K. Perturbation theory approach to intermolecular potential energy surfaces of van der waals complexes. Chem. Rev., 1994, 94, 1887-1930.
 
[50]  Bale, T. L.; Vale, W. W. CRF and CRF receptors: role in stress responsivity and other behaviors. Annu. Rev. Pharmacol. Toxicol., 2004, 44, 525-557.
 
[51]  López de Victoria A, Tamamis P, Kieslich CA, Morikis D. Insights into the structure, correlated motions, and electrostatic properties of two HIV-1 gp120 V3 loops., PLOS ONE 2012, 7, e49925.
 
[52]  Lou J, Smith RJ. Modelling the effects of adherence to the HIV fusion inhibitor enfuvirtide. J. Theor. Biol., 2011, 268, 1-13.
 
[53]  Cosconati, S.; Marinelli, L.; Lavecchia, A.; Novellino, E. Characterizing the 1,4-dihydropyridines binding interactions in the l-type ca2+ channel: model construction and docking calculations J. Med. Chem., 2007, 50, 1504-1513.
 
[54]  Storici, P.; De Biase, D.; Bossa, F.; Bruno, S.; Mozzarelli, A.; Pene_, C. Structures of γ-aminobutyric acid (GABA) aminotransferase, a pyridoxal 5′-phosphate, and [2Fe-2S] cluster-containing enzyme, complexed with γ-ethynyl-GABA and with the antiepilepsy drug vigabatrin. J. Biol. Chem., 2004, 279, 363-373.
 
[55]  Huey, R.; Morris, G. M. Using AutoDock 4 with AutoDocktools: a tutorial. La Jolla, CA, USA: The Scripps Research Institute, Molecular Graphics Laboratory, 2008, pp. 54-56.
 
[56]  Davood, A.; Nematollahi, A.; Iman, M.; Shafiee, A. Synthesis and docking studies of new 1,4-dihydropyridines containing 4-(5)-Chloro-2-ethyl-5-(4)-imidazolyl substituent as novel calcium channel agonist. Arch. Pharm. Res., 2009, 32, 481-487.
 
[57]  O'Reilly, A. O.; Khambay, B. P. S.; Williamson, M. S.; Field, L. M.; Wallace, B. A. Modelling insecticide-binding sites in the voltage-gated sodium channel. Biochem. J., 2006, 396, 255-263.
 
[58]  Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S. H.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D. A.; Cheatham, T. E. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res., 2000, 33, 889-897.
 
[59]  Case, D. A.; Cheathham, T. E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B., Woods, R. J. Comput. Chem., 2005, 26, 1668-1688.
 
[60]  Rocchia, W.; Alexov, E.; Honig, B. Extending the applicability of the nonlinear poisson-boltzmann equation: multiple dielectric constants and multivalent ions. J. Phys. Chem. B, 2001, 105, 6507-6514.
 
[61]  Du, J.; Sun, H.; Xi, L.; Li, J.; Yang, Y.; Liu, H.; Yao, X. Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM-GBSA calculation. J. Comput. Chem., 2011, 32, 2800-2809.
 
[62]  Zheng, F.; Zhan, C.-G. Computational modeling of solvent effects on protein-ligand interactions using fully polarizable continuum model and rational drug design. Commun. Comput. Phys., 2013, 13, 31-60.
 
[63]  Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47, 1739-1749.
 
[64]  Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 1998, 19, 1639-1662
 
[65]  Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 261, 470-489.
 
[66]  Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 1997, 267, 727-748.
 
[67]  Wang, R. X.; Lu, Y. P.; Wang, S. M. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem., 2003, 46, 2287-2303.
 
[68]  Cummings, M. D.; DesJarlais, R. L.; Gibbs, A. C.; Mohan, V.; Jaeger, E. P. Comparison of automated docking programs as virtual screening tools. J. Med. Chem., 2005, 48, 962-976.
 
[69]  Kontoyianni, M.; McClellan, L. M.; Sokol, G. S. Evaluation of docking performance: comparative data on docking algorithms. J. Med. Chem., 2004, 47, 558-565.
 
[70]  Kontoyianni, M.; Sokol, G. S.; McClellan, L. M. Evaluation of library ranking efficacy in virtual screening. J. Comput. Chem. 2005, 26, 11-22.
 
[71]  Perola, E.; Walters, W. P.; Charifson, P. S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins, 2004, 56, 235-249.
 
[72]  Wang, R. X.; Lu, Y. P.; Fang, X. L.; Wang, S. M. An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J. Chem. Inf. Comput. Sci., 2004, 44, 2114-2125.
 
[73]  Bissantz, C.; Folkers, G.; Rognan, D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J. Med. Chem., 2000, 43, 4759-4767.
 
[74]  Paul, N.; Rognan, D. ConsDock, a new program for the consensus analysis of protein-ligand interactions. Proteins, 2002, 47, 521-533.
 
[75]  Weis, A.; Katebzadeh, K.; Söderhjelm, P.; Nilsson, I.; Ryde, U. Ligand affinities predicted with the mm/pbsa method: dependence on the simulation method and the force field. J. Med. Chem., 2006, 49, 6596-6606.
 
[76]  Wang, J. M.; Hou, T. J.; Xu, X. J. Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Curr. Comput.-Aided Drug Des., 2006, 2, 287-306.
 
[77]  Wang, W.; Donini, O.; Reyes, C. M.; Kollman, P. A. Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein−ligand, protein−protein, and protein−nucleic acid noncovalent interactions. Annu. Rev. Biophys. Biomol. Struct., 2001, 30, 211-243.
 
[78]  Muegge, I. Effect of ligand volume correction on PMF scoring. J. Comput. Chem., 2001, 22, 418-425.
 
[79]  Muegge, I.; Martin, Y. C. A general and fast scoring function for protein-ligand interactions: A simplified potential approach. J. Med. Chem., 1999, 42, 791-804.
 
[80]  Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein−ligand interactions. J. Mol. Biol., 2000, 295, 337-356.
 
[81]  Lyne, P. D.; Lamb, M. L.; Saeh, J. C. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J. Med. Chem., 2006, 49, 4805-4808.
 
[82]  Guimaraes, C. R. W.; Cardozo, M. MM-GB/SA rescoring of docking poses in structure-based lead optimization. J. Chem. Inf. Model., 2008, 48, 958-970.
 
[83]  Kuhn, B.; Gerber, P.; Schultz-Gasch, T.; Stahl, M. Validation and use of the MM-PBSA approach for drug discovery. J. Med. Chem., 2005, 48, 4040-4048.
 
[84]  Hou, T. J.; Wang, J. M.; Li, Y. Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model., 2011, 51, 69-82.
 
[85]  Hou, T. J.; Wang, J. M.; Li, Y. Y.; Wang, W. Assessing the performance of the molecular mechanics Poisson Boltzmann surface area and molecular mechanics generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J. Comput. Chem., 2011, 32, 866-877.
 
[86]  Zhang, D. W.; Zhang, J. Z. H. Molecular fractionation with conjugate caps for full quantum mechanical calculation of protein−molecule interaction energy. J. Chem. Phys., 2003, 119, 3599-3606.
 
[87]  Gao, A. M.; Zhang, D. W.; Zhang, J. Z. H.; Zhang, Y. K. An efficient linear scaling method for ab initio calculation of electron density of proteins. Chem. Phys. Lett., 2004, 394, 293-297.
 
[88]  Mei, Y.; Ji, C. G.; Zhang, J. Z. H. A new quantum method for electrostatic solvation energy of protein. J. Chem. Phys., 2006, 125, No. 094906.
 
[89]  Ji, C. G.; Mei, Y.; Zhang, J. Z. H. Developing polarized protein specific charges for protein dynamics: MD free energy calculation of pKa shifts for Asp26/Asp20 in thioredoxin. Biophys. J., 2008, 95, 1080-1088.
 
[90]  Tong, Y.; Mei, Y.; Zhang, J. Z. H.; Duan, L. L.; Zhang, Q. G. Quantum calculation of protein solvation and protein-ligand binding free energy for HIV-1 protease/water complex. J. Theor. Comput. Chem., 2009, 8, 1265-1279.
 
[91]  McConkey, B. J.; Sobolev, V.; Edelman, M. The performance of current methods in ligand-protein docking. Curr. Sci., 2002, 83, 845-856.
 
[92]  de Graaf, C.; Pospisil, P.; Pos, W.; Folkers, G.; Vermeulen, N. P. E. Binding mode prediction of cytochrome P450 and thymidine kinase protein-ligand complexes by consideration of water and rescoring in automated docking. J. Med. Chem., 2005, 48, 2308-2318.
 
[93]  Poornima, C. S.; Dean, P. M. Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions. J. Comput.-Aided Mol. Des., 1995, 9, 500-512.
 
[94]  Poornima, C. S.; Dean, P. M. Hydration in drug design. 2. Influence of local site surface shape on water binding. J. Comput.-Aided Mol.Des., 1995, 9, 513-520.
 
[95]  Poornima, C. S.; Dean, P. M. Hydration in drug design. 3. Conserved water molecules at the ligand-binding sites of homologous proteins. J. Comput.-Aided Mol. Des., 1995, 9, 521-531.
 
[96]  Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra precision Glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J. Med. Chem., 2006, 49, 6177-6196.
 
[97]  Young, T.; Abel, R.; Kim, B.; Berne, B. J.; Friesner, R. A. Motifs for molecular recognition exploiting hydrophobic enclosure in protein−ligand binding. Proc. Natl. Acad. Sci. U.S.A., 2007, 104, 808-813.
 
[98]  Abel, R.; Young, T.; Farid, R.; Berne, B. J.; Friesner, R. A. Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J. Am. Chem. Soc., 2008, 130, 2817-2831.
 
[99]  Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. The particle concept: placing discrete water molecules during protein-ligand docking predictions. Proteins, 1999, 34, 17-28.
 
[100]  Schnecke, V.; Kuhn, L. A. Virtual screening with alvation and ligand-induced complementarity. Perspect. Drug Discov. Des., 2000, 20,171-190.
 
[101]  Osterberg, F.; Morris, G. M.; Sanner, M. F.; Olson, A. J.; Goodsell, D. S. Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins, 2002, 46, 34-40.
 
[102]  Verdonk, M. L.; Chessari, G.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W. Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem., 2005, 48, 6504-6515.
 
[103]  Böhm, H.-J.;Klebe, G. What can we learn from molecular recognition in protein-ligand complexes for the design of new drugs? Angew. Chem. Int. Ed. Engl., 1996, 3S, 2588-2614.
 
[104]  Uchida, T.; Ishimori, K.; Morishima, I. The effects of heme pocket hydrophobicity on the ligand binding dynamics in myoglobin as studied with leucine 29 mutants. J. Biol. Chem., 1997, 272, 30108-30114.
 
[105]  Setny, P.; Baron,R.; Kekenes-Huskey, P. M.; McCammon, J. A.; Dzubiella, J. Solvent fluctuations in hydrophobic cavity-ligand binding kinetics. Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 1197-1202.
 
[106]  Ahmad, M.; Gu, W.; Helms, V. Mechanism of fast peptide recognition by SH3 domains. Angew. Chem. Int. Ed. Engl., 2008, 47, 7626-7630.
 
[107]  Setny, P.; Wang, Z.; Cheng, L.-T.; Li, B.; McCammon, J. A.; Dzubiella, J. Dewetting-controlled binding of ligands to hydrophobic pockets. Phys. Rev. Lett., 2009, 103, 187801.
 
[108]  Han, S.; Zaniewski, R. P.; Marr, E. S.; Lacey, B. M.; Tomaras, A. P.; Evdokimov, A.; Miller,J. R.; Shanmugasundaram, V. Structural basis for effectiveness of siderophore-conjugated monocarbams against clinically relevant strains of Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. U. S. A,. 2010, 107, 22002-22007.
 
[109]  Liu, L.; Michelsen, K.; Kitova, E. N.; Schnier, P. D.; Klassen, J. S. Evidence that water can reduce the kinetic stability of protein-hydrophobic ligand interactions. J. Am. Chem. Soc., 2010, 132, 17658-17660.
 
[110]  Dror, R. O. C. Pan, A. C.; Arlow, D. H.; Borhani, D. W.; Maragakis, P.; Shan, Y.; Xu, H.; Shaw, D. E. Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc. Natl. Acad. Sci. U. S. A., 2011, 108, 13118-13123.
 
[111]  Schmidtke, P.; Luque, F. J.; Murray, J. B.; Barril, X. Shielded hydrogen bonds as structural determinants of binding kinetics: application in drug design. J. Am. Chem. Soc., 2011, 133, 18903-18910.
 
[112]  Chandler, D. Interfaces and the driving force of hydrophobic assembly. Nature, 2005, 437, 640-647.
 
[113]  Dror, R.O.; Jensen, M. Ø.; Borhani, D. W.; Shaw, D. E. Exploring atomic resolution physiology on a femtosecond to millisecond timescale using molecular dynamics simulations. J. Gen. Phys., 2010, 135, 555-562.
 
[114]  Buch, I.; Giorgino, T.; De Fabritiis, G. Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc. Natl. Acad. Sci. U. S. A., 2011, 108, 10184-10189.
 
[115]  Shan, Y.; Kim, E. T.; Eastwood, M. P.; Dror, R. O.; Seeliger, M. A.; Shaw, D. E. How does a drug molecule find its target binding site? J. Am. Chem. Soc., 2011, 133, 9181-9183.
 
[116]  Chang, C.E. A.; Trylska, J.; Tozzini, V.; McCammon, J. Binding pathways of ligands to HIV-1 protease: coarse grained and atomistic simulations. Chem. Biol. Drug Des., 2007, 69, 5-13.
 
[117]  Li, X.; Latour, R. A.; Stuart, S. J. TIGER2: an improved algorithm for temperature intervals with global exchange of replicas. J. Chem. Phys., 2009. 130, 174106.
 
[118]  Kang, M. Roberts, C.; Cheng, Y.; Chang, C.-E. A. Gating and intermolecular interactions in ligand-protein association: coarse-grained modeling of HIV-1 protease. J. Chem. Theory Comput., 2011, 7, 3438-3446.
 
[119]  Kruse, A.C.; Hu, J.; Pan, A. C.; Arlow, D. H.; Rosenbaum, D. M.; Rosemond, E.; Green, H. F.; Liu, T.; Chae, P. S.; Dror, R. O.; Shaw, D.E.; Weis, W. I.; Wess, J.; Kobilka, B. K. Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature, 2012, 482, 552-556.
 
[120]  Carlsson, P.; Burendahl, S.; Nilsson, L. Unbinding of retinoic acid from the retinoic acid receptor by random expulsion molecular dynamics. Biophys. J., 2006, 91, 3151-3161.
 
[121]  Wang, T.; Duan, Y. Ligand entry and exit pathways in the b2-adrenergic receptor. J. Mol. Biol., 2009, 392, 1102-1115.
 
[122]  Pietrucci, F.; Marinelli, F.; Carloni, P.; Laio, A. Substrate binding mechanism of HIV-1 protease from explicit-solvent atomistic simulations. J. Am. Chem. Soc., 2009, 131, 11811-11818.
 
[123]  Colizzi, F.; Perozzo, R.; Scapozza, L.; Recanatini, M.; Cavalli, A. Single-molecule pulling simulation can discern active from inactive enzyme inhibitors. J. Am. Chem. Soc., 2010, 132, 7361-7371.
 
[124]  Limongelli, V.; Bonomi, M.; Marinelli, L.; Gervasio, F. L.; Cavalli, A.; Novellino, E.; Parrinello, M. Molecular basis of cyclooxygenase enzymes (COXs) selective inhibition. Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 5411-5416.
 
[125]  Bortolato, A.; Deflorian, F.; Weiss, D. R.; Mason, J. S. Decoding the role of water dynamics in ligand−protein unbinding: CRF1R as a test case. J. Chem. Inf. Model., 2015, 55, 1857-1866
 
[126]  Luckner, S.R.; Liu, N.; am Ende, C. W.; Tonge, P. J.; Kisker, C. A slow, tight binding inhibitor of InhA, the enoyl-acyl carrier protein reductase from Mycobacterium tuberculosis. J. Biol. Chem., 2010, 285, 14330-14337
 
[127]  Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. L. III. Assessing scoring functions for protein-ligand interactions. J. Med. Chem., 2004, 47, 3032-3047.
 
[128]  Bliznyuk, A A.; Rendell, A. P. Electronic effects in biomolecular simulations: Investigation of the KcsA potassium ion channel. J. Phys. Chem. B, 2004, 108, 13866-13873.
 
[129]  Bliznyuk, A. A.; Rendell, A. P.; Allen, T. W.; Chung, S. H. The potassium ion channel: comparison of linear scaling semiempirical and molecular mechanics representations of the electrostatic potential. J. Phys. Chem. B, 2001, 105, 12674-12679.
 
[130]  Zuegg, J.; Bliznyuk, A. A.; Gready, J. E. Comparison of electrostatic potential around proteins calculated from Amber and AM1 charges: application to mutants of prion protein. Mol. Phys., 2003, 101, 2437-2450.
 
[131]  Raha, K.; Merz, K. M. Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J. Med. Chem., 2005, 48, 4558-4575.
 
[132]  Vasilyev, V.; Bliznyuk, A. Application of semiempirical quantum chemical methods as a scoring function in docking. Theor. Chem. Acc., 2004, 112, 313-317.
 
[133]  Villar, R.; Gil, M. J.; Garcia, J. I.; Martinez-Merino, V. Are AM1 ligand-protein binding enthalpies good enough for use in the rational design of new drugs? J. Comput. Chem., 2005, 26, 1347-1358.
 
[134]  Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, W. P. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem., 1999, 42, 5100-5109.
 
[135]  Clark, R. D.; Strizhev, A.; Leonard, J. M.; Blake, J. F.; Matthew, J. B. Consensus scoring for ligand/protein interactions. J. Mol. Graph. Model., 2002, 20, 281-295.
 
[136]  Perez, C.; Ortiz, A. R. Evaluation of docking functions for protein-ligand docking. J. Med. Chem., 2001, 44, 3768-3785.
 
[137]  Czerminski, R.; Elber, R. Computational studies of ligand diffusion in globins: I. Leghemoglobin. Proteins, 1991, 10, 70-80.
 
[138]  Sugita, Y.; Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett., 1999, 314, 141-151.
 
[139]  Berndt, K. D.; Guntert, P.; Wuthrich, K. Conformational sampling by NMR solution structures calculated with the program DIANA evaluated by comparison with long-time molecular dynamics calculations in explicit water. Proteins, 1996, 24, 304-313.
 
[140]  Brooijmans, N.; Kuntz, I. D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32, 335-373.
 
[141]  Apostolakis, J.; Pluckthun, A.; Caflisch, A. Docking small ligands in flexible binding sites. J. Comput. Chem., 1998, 19, 21-37.
 
[142]  Claussen, H.; Buning, C.; Rarey, M.; Lengauer, T. FlexE: efficient molecular docking considering protein structure variations. J. Mol. Biol., 2001, 308, 377-395.
 
[143]  Trosset, J. Y.; Scheraga, H. A. PRODOCK: Software package for protein modeling and docking. J. Comput. Chem., 1999, 20, 412-427.
 
[144]  Carlson, H. A.; Masukawa, K. M.; Rubins, K.; Bushman, F. D.; Jorgensen, W. L.; Lins, R. D.; Briggs, J. M.; McCammon, J. A. Developing a dynamic pharmacophore model for HIV-1 integrase. J. Med. Chem., 2000, 43, 2100-2114.
 
[145]  Alonso, H.; Bliznyuk, A. A.; Gready, J. E. Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev., 2006, 26, 531-568.
 
[146]  Leach, A. R. Ligand docking to proteins with discrete side-chain flexibility. J. Mol. Biol., 1994, 235, 345-356.
 
[147]  Aqvist, J.; Medina, C.; Sammuelsson, J. E. A new method for predicting binding affinity in computer-aided drug design. Protein Eng., 1994, 7, 385-391.
 
[148]  Huo, S.; Wang, J.; Cieplak, P.; Kollman, P. A.; Kuntz, I. D. Molecular dynamics and free energy analyses of cathepsin D-inhibitor interactions: insight into structure-based ligand design. J. Med. Chem., 2002, 45, 1412-1419.
 
[149]  Kuhn, B.; Kollman, P. A. Binding of a diverse set of ligands to avidin and streptavidin: An accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. J. Med. Chem., 2000, 43, 3786-3791.
 
[150]  Smith, R. H. Jr; Jorgensen, W. L.; Tirado-Rives, J.; Lamb, M. L.; Janssen, P. A.; Michejda, C. J.; Kroeger Smith, M. B. Prediction of binding affinities for TIBO inhibitors of HIV-1 reverse transcriptase using Monte Carlo simulations in a linear response method. J. Med. Chem., 1998, 41, 5272-5286.
 
[151]  Wang, J.; Morin, P.; Wang, W.; Kollman, P. A. Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J. Am. Chem. Soc. 2001, 123, 5221-5230.
 
[152]  Straatsma, T. P.; McCammon J. A. Computational alchemy. Ann. Rev.Phys. Chem., 1992, 43, 407-435.
 
[153]  Brandsdal, B. O.; Österberg, F.; Almlöf, M.; Feierberg, I.; Luzhkov, V. B.; Åqvist, J. Free energy calculations and ligand binding. Adv. Protein Chem., 2003, 66, 123-158.
 
[154]  Beveridge, D. L.; DiCapua, F. M. Free energy via molecular simulation: Applications to chemical and biomolecular systems. Annu. Rev. Biophys. Biophys. Chem., 1989, 18, 431-492.
 
[155]  Gready, J. E.; Cummins, P. L. Mechanism-based substrates and inhibitors of dihydrofolate reductase. In: Reddy, M. R.; Erion, M. D., editors. Free energy calculations in rational drug design. New York: Kluwer Academic/Plenum, 2001, p 343-364.
 
[156]  Bash, P. A.; Singh, U. C.; Brown, F. K.; Langridge, R.; Kollman, P. A. Calculation of the relative change in binding free energy of a protein-inhibitor complex. Science, 1987, 235, 574-576.
 
[157]  Aqvist ,J. Calculation of absolute binding free energies for charged ligands and effects of long-range electrostatic interactions. J. Comput. Chem., 1996, 17, 1587-1597.
 
[158]  Hansson, T.; Aqvist, J. Estimation of binding free energies for HIV proteinase inhibitors by molecular dynamics simulations. Protein. Eng., 1995, 8, 1137-1144.
 
[159]  Hansson, T.; Marelius, J.; Aqvist, J. Ligand binding affinity prediction by linear interaction energy methods. J. Comput. Aided Mol. Des., 1998, 12, 27-35.
 
[160]  Wang, W.; Wang, J.; Kollman, P. A. What determines the van der Waals coefficient beta in the LIE (linear interaction energy) method to estimate binding free energies using molecular dynamics simulations? Proteins, 1999, 34, 395-402.
 
[161]  Swanson, J. M.; Henchman, R. H.; McCammon, J. A. Revisiting free energy calculations: a theoretical connection to MM/PBSA and direct calculation of the association free energy. Biophys. J., 2004, 86, 67-74.
 
[162]  Aqvist, J.; Luzhkov, V. B.; Brandsal, B. O. Ligand binding affinities from MD simulations. Acc. Chem. Res., 2002, 35, 358-365.
 
[163]  Paulsen, M. D.; Ornstein, R. L. Binding free energy calculations for P450cam-substrate complexes. Protein. Eng., 1996, 9, 567-571.
 
[164]  Wang, J.; Dixon, R.; Kollman, P. A. Ranking ligand binding affinities with avidin: a molecular dynamics-based interaction energy study. Proteins: Struct. Funct. Genet., 1999, 34, 69-81.
 
[165]  Jones-Hertzog, D. K.; Jorgensen, W. L. Binding affinities for sulfonamide inhibitors with human thrombin using Monte Carlo simulations with a linear response method. J. Med. Chem., 1997, 40, 1539-1549.
 
[166]  Carlson, H. A.; Jorgensen, W. L. An extended linear-response method for determining free-energies of hydration. J. Phys. Chem., 1995, 99, 10667-10673.
 
[167]  Aqvist, J.; Marelius, J. The linear interaction energy method for predicting ligand binding free energies. Comb. Chem. High Throughput Screen, 2001, 4, 613-626.
 
[168]  Almlof, M.; Brandsdal, B. O.; Aqvist, J. Binding affinity prediction with different force fields: examination of the linear interaction energy method. J. Comput. Chem., 2004, 25, 1242-1254.
 
[169]  Norberg, J.; Nilsson, L. Advances in bimolecular simulations: methodology and recent applications. Q. Rev. Biophys., 2003, 36, 257-306.
 
[170]  Tai, K. Conformational sampling for the impatient. Biophys. Chem., 2004, 107, 213-220.
 
[171]  Miranker, A.; Karplus, M. Functionality maps of binding sites: A multiple copy simultaneous search method. Proteins, 1991, 11, 29-34.
 
[172]  Huber, T.; Torda, A. E.; van Gunsteren, W. F. Local elevation: A method for improving the searching properties of molecular dynamics simulation. J. Comput. Aided Mol. Des., 1994, 8, 695-708.
 
[173]  Schulze, B. G.; Grubmuller, H.; Evanseck, J. D. Functional significance of hierarchical tiers in carbon monoxy myoglobin: conformational substates and transitions studied by conformational flooding simulations. J. Am. Chem. Soc., 2000, 122, 8700-8711.
 
[174]  Guvench, O.; MacKerell, A. D. Jr. Computational fragment-based binding site identification by ligand competitive saturation. PLoS Comput. Biol., 2009, 5, e1000435.
 
[175]  Seco, J.; Luque, F.J.; Barril, X. Binding site detection and druggability index from first principles. J. Med. Chem., 2009, 52, 2363-2371.
 
[176]  Raman, E.P.; Yu, W.; Guvench, O.; Mackerell, A.D. Reproducing crystal binding modes of ligand functional groups using site-identification by ligand competitive saturation (SILCS) simulations. J. Chem. Inf. Model., 2011, 51, 877-896.
 
[177]  Gervasio, F. L.; Laio, A.; Parrinello, M. Flexible docking in solution using metadynamics. J. Am. Chem. Soc., 2005, 127, 2600-2607.
 
[178]  Laio, A.; Parrinello, M. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA, 2002, 99, 12562-12566.
 
[179]  Bieri, M.; Kwan, A. H.; Mobli, M.; King, G. F.; Mackay, J. P.; Gooley, P. R. Macromolecular NMR spectroscopy for the non-spectroscopist: beyond macromolecular solution structure determination. FEBS J., 2011, 278, 704-715
 
[180]  Kwan, A. H.; Mobli, M.; Gooley, P. R.; King, G. F.; Mackay, J. P. Macromolecular NMR spectroscopy for the non-spectroscopist. FEBS J. ,2011, 278, 687-703
 
[181]  Kasai, K.; Ishii, S. J. Quantitative analysis of affinity chromatography of trypsin. A new technique for investigation of protein-ligand interaction. Biochem, 1975, 77, 261-264.
 
[182]  Eldridge, M. D.; Murray, C. W.; Auton, T. R.; Paolini, G. V.; Mee, R. P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput.Aided Mol. Des., 1997, 11, 425-445.
 
[183]  Richarme, G.; Kepes, A. Study of binding protein-ligand interaction by ammonium sulfate-assisted adsorption on cellulose esters filters. Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol. 1983, 742, 16-24.
 
[184]  Muegge, I.; Martin, Y. C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J. Med. Chem., 1999, 42, 791-804.
 
[185]  Olsson, T. S. G.; Williams, M. A.; Pitt, W. R.; Ladbury, J. E. The thermodynamics of protein-ligand interaction and solvation: insights for ligand design. J. Mol. Biol., 2008, 384, 1002-1017.
 
[186]  Bash, P. A.; Field, M. J.; Karplus, M. Free energy perturbation method for chemical reactions in the condensed phase: a dynamic approach based on a combined quantum and molecular mechanics potential. J. Am. Chem. Soc., 1987, 109, 8092-8094.
 
[187]  Rao, S. N.; Singh, U. C.; Bash, P. A.; Kollman, P. A. Free energy perturbation calculations on binding and catalysis after mutating Asn 155 in subtilisin. Nature, 1987, 328, 551-554.
 
[188]  Kita, Y.; Arakawa, T.; Lin, T. Y.; Timasheff, S. N. Contribution of the surface free energy perturbation to protein-solvent interactions. Biochemistry, 1994, 33, 15178-15189.
 
[189]  Rao, B. G.; Singh, U. C. A free energy perturbation study of solvation in methanol and dimethyl sulfoxide. J. Am. Chem. Soc., 1990, 112, 3803-3811.
 
[190]  Kollman, P. A. Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev., 1993, 93, 2395-2417.
 
[191]  Jorgensen, W. L.; Thomas, L. L. Perspective on Free-Energy Perturbation Calculations for Chemical Equilibria. J. Chem. Theory Comput., 2008, 4, 869-876.
 
[192]  Zacharias, M.; Straatsma, T. P.; McCammon, J. A. Separation-shifted scaling, a new scaling method for Lennard-Jones interactions in thermodynamic integration. J. Chem. Phys., 1994, 100, 9025-9031.
 
[193]  Ryde, U.; Söderhjelm, P. Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev., 2016, 116, 5520-5566.
 
[194]  Massova, I.; Kollman, P. A. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discovery Des., 2000, 18, 113-135.
 
[195]  Luo, R.; David, L.; Gilson, M. K. Accelerated Poisson-Boltzmann calculations for static and dynamic systems. J. Comput. Chem., 2002, 23, 1244-1253.
 
[196]  Nicholls, A.; Honig, B. A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation. J. Comput. Chem., 1991, 12, 435-445.
 
[197]  Chen, Z.; Baker, N. A.; Wei, G. W. J. Comput. Phys., 2010, 229, 8231-8258.
 
[198]  Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.;, Head, M. S. A critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49, 5912-5931.
 
[199]  Cross, J. B.; Thompson, D. C.; Rai, B. K.; Baber, J. C.; Fan, K. Y.; Hu, Y.; Humblet, C. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J. Chem. Inf. Model., 2009, 49, 1455-1474.
 
[200]  Lengauer, T.; Rarey, M. Computational methods for biomolecular docking. Curr. Opin. Struct. Biol., 1996, 6, 402-406.
 
[201]  Jorgensen, W. L. Efficient drug lead discovery and optimization. Acc. Chem. Res., 2009, 42, 724-733.
 
[202]  Rosenfeld, R. J.; Goodsell, D. S.; Musah, R. A.; Morris, G. M.; Goodin, D. B.; Olson A. J. Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling. J. Comput. Aided Mol. Des., 2003, 17, 525-536.
 
[203]  Goodsell, D. S.; Morris, G. M.; Olson, A. J. Automated docking of flexible ligands: applications of AutoDock. J. Mol. Recognit., 1996, 9, 1-5.
 
[204]  Ewing, T. J. A.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des., 2001, 15, 411-428.
 
[205]  Kramer, B.; Rarey, M.; Lengauer, T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins, 1999, 37, 228-241.
 
[206]  Yang, J. M. Development and evaluation of a generic evolutionary method for protein-ligand docking. J. Comput. Chem., 2004, 25, 843-857.
 
[207]  Yang, J. M.; Chen, C. C. GEMDOCK: a generic evolutionary method for molecular docking. Proteins, 2004, 55, 288-304.
 
[208]  Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 2004, 47, 1750-1759.
 
[209]  Kirkpatrick, P. Virtual screening: gliding to success. Nat. Rev. Drug. Discov., 2004, 3, 299-299.
 
[210]  Krovat, E. M.; Steindl, T.; Langer, T. Recent advances in docking and scoring. Curr. Comput. Aided Drug Des., 2005, 1, 93-102.
 
[211]  Verdonk, M. L.;, Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Improved protein-ligand docking using GOLD. Proteins, 2003, 52, 609-623.
 
[212]  Joy, S.; Nair, P. S.; Hariharan, R.; Pillai, M. R. Detailed comparison of theprotein-ligand docking effciencies of GOLD, a commercial package and ArgusLab, a licensable freeware. In Silico Biol., 2006, 6, 601-605.
 
[213]  Thomsen, R.; Christensen, M. H. MolDock: A new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49, 3315-3321.
 
[214]  Jain, A. N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem., 2003, 46, 499-511.
 
[215]  Mitrasinovic, P. M. On the structure-based design of novel inhibitors of H5N1 influenza A virus neuraminidase (NA). Biophys. Chem., 2009, 140, 35-38.
 
[216]  Mihajlovic, M. L.; Mitrasinovic, P. M. Applications of the ArgusLab 4/A Score protocol in the structure-based binding affinity prediction of various inhibitors of group-1 and group-2 influenza virus neuraminidases (NAs). Mol. Simul., 2009, 35, 311-324.
 
[217]  Ravindranathan, K. P.; Mandiyan, V.; Ekkati, A. R.; Bae, J. H.; Schlessinger, J,; Jorgensen, W. L. Discovery of novel fibroblast growth factor receptor 1 kinase inhibitors by structure-based virtual screening. J. Med. Chem., 2010, 53, 1662-1672.
 
[218]  Johnson, B. C.; Métifiot, M.; Pommier, Y.; Hughesa, S. H. Molecular dynamics approaches estimate the binding energy of HIV-1 integrase inhibitors and correlate with in vitro activity. Antimic. Ag. Chemother., 2012, 56, 411-419.
 
[219]  Langley, J. N. On the reaction of cells and of nerve-endings to certain poisons, cheifly as regards the reaction of striated muscle to nicotine and to curari. J. Physiol., 1905, 33, 374-413.
 
[220]  Copeland, R. A.; Pompliano, D. L.; Meek, T. D. Drug-target residence time and its implications for lead optimization. Nat. Rev. Drug Discov., 2006, 5, 730-739.
 
[221]  Swinney, D. C. The role of binding kinetics in therapeutically useful drug action. Curr. Opin. Drug Discov. Dev., 2009, 12, 31-39.
 
[222]  Lu, H.; Tonge, P. J. Drug-target residence time: critical information for lead optimization. Curr. Opin. Chem. Biol., 2010, 14, 467-474.
 
[223]  Waszkowycz, B.; Clark, D. E.; Gancia, E. Outstanding challenges in protein-ligand docking and structure-based virtual screening. WIREs Comput. Mol. Sci., 2011, 1, 229-259.
 
[224]  Coupez, B.; Lewis, R. Docking and scoring-theoretically easy, practically impossible? Curr. Med. Chem., 2006, 13, 2995-3003.
 
[225]  Alberts, B. The cell as a collection overview of protein machines: preparing the next generation of molecular biologists. Cell, 1998, 92, 291-294.
 
[226]  Seifriz, W. A materialistic interpretation of life. Phil. Sci., 1939, 6, 266-284.
 
[227]  Cavasotto, C. N.; Abagyan, R. A. Protein flexibility in ligand docking and virtual screening to protein kinases. J. Mol. Biol., 2004, 337, 209-226.
 
[228]  Chandrika, B.; Subramanian, J.; Sharma, S. D. Managing protein flexibility in docking and its applications. Drug Discov. Today, 2009, 14, 394-400.
 
[229]  Carlson, H. A. Protein flexibility is an important component of structure-based drug discovery. Curr. Pharm. Des., 2002, 8, 1571-1578.
 
[230]  Tuffery, P.; Derreumaux, P. Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. J. R. Soc. Interface, 2012, 9, 20-33.
 
[231]  Guo, D.; Thea Mulder-Krieger, T.; Jzerman, A. P. I.; Heitman, L. H. Functional efficacy of adenosine A2A receptor agonists is positively correlated to their receptor residence time. Br. J. Pharmacol., 2012, 166, 1846-1859.
 
[232]  Lipton, S. A. Turning down, but not off: neuroprotection requires a paradigm shift in drug development. Nature, 2004, 428, 473-474.
 
[233]  Ohlson, S. Designing transient binding drugs: a new concept for drug discovery. Drug Discov. Today, 2008, 13, 433-439.
 
[234]  Vauquelin, G.; Bostoen, S.; Vanderheyden, P.; Seeman, P. Clozapine, atypical antipsychotics, and the benefits of fast-off D2 dopamine receptor antagonism. Naunyn-Schmiedeberg’s Arch. Pharmacol., 2012, 385, 337-372.
 
[235]  Keighley, W. The need for high throughput kinetics early in the drug discovery process. Drug Disc. World Summer, 2011, 39-45.
 
[236]  Frauenfelder, H.; Sligar, S. G.; Wolynes, P. G. The energy landscapes and motions of proteins. Science, 1991, 254, 1598-1603.
 
[237]  Uchida, T.; Unno, M.; Ishimori, K.; Morishima, I. Effects of the intramolecular disulfide bond on ligand binding dynamics in myoglobin. Biochemistry, 1997, 36, 324-332.
 
[238]  Pargellis, C.; Tong, L.; Churchill, L.; Cirillo, P.F.; Gilmore, T.; Graham; A. G.; Grob, P. M.; Hickey, E. R.; Moss, N.; Pav, S.; Regan, J. Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site. Nat. Struct. Biol., 2002, 9, 268-272.
 
[239]  Millan, D.S. Mark E. Bunnage, M. E.; Burrows, J. L.; Butcher, K. J.; Dodd, P. G.; Evans, T. J.; Fairman, D. A.; Hughes, S. J.; Kilty, I. C.; Lemaitre, A.; Lewthwaite, R. A.; Mahnke, A.; Mathias, J. P.; Philip, J.; Smith, R. T.; Stefaniak, M. H.; Yeadon, M.; Phillips, C. Design and synthesis of inhaled p38 inhibitors for the treatment of chronic obstructive pulmonary disease. J. Med. Chem., 2011, 54, 7797-7814.
 
[240]  Robinson, R.A. and Stokes, R.H. Electrolyte Solutions (2nd revised edn.), 2002, Dover Publications Inc., Mineola NY, USA.
 
[241]  Sun, S.F., ed. Physical Chemistry of Macromolecules: Basic Principles and Issues. Second Edition. John Wiley and Sons, Inc., 2004, Hoboken, New Jersey.
 
[242]  Schreiber, G.; Fersht, A. R. Rapid, electrostatically assisted association of proteins. Nat. Struct. Biol., 1996, 3, 427-431.
 
[243]  Fedosova, N. U.; Champeil, P.; Esmann, M. Nucleotide binding to Na, K-ATPase: the role of electrostatic interactions. Biochemistry, 2002, 41, 1267-1273.
 
[244]  Radić, Z.; Kirchhoff, P. D.; Quinn, D. M.; McCammon, J. A.; Taylor, P. Electrostatic influence on the kinetics of ligand binding to acetylcholinesterase. Distinctions between active center ligands and fasciculin. J. Biol. Chem., 1997, 272, 23265-23277.
 
[245]  Collins, K. D. Why continuum electrostatics theories cannot explain biological structure, polyelectrolytes or ionic strength effects in ion-protein interactions. Biophys. Chem., 2012, 167, 43-59.
 
[246]  Ledvina, P. S.; Tsai, A. L.; Wang, Z.; Koehl, E.; Quiocho, F. A. Dominant role of local dipolar interactions in phosphate binding to a receptor cleft with an electronegative charge surface: equilibrium, kinetic, and crystallographic studies. Protein Science, 1998, 7, 2550-2559.
 
[247]  Krishnamurthy, V. M.; Kaufman, G. K.; Urbach, A. R.; Gitlin, I.; Gudiksen, K. L.; Weibel, D. B.; Whitesides, G. M. Carbonic anhydrase as a model for biophysical and physical-organic studies of proteins and protein-ligand binding. Chem. Rev., 2008, 103, 946-1051.
 
[248]  Ryde, U. On the role of covalent strain in protein function. Recent Research Developments in Protein Engineering, Research Signpost: Trivandrum, India, 2002; 2, 65-91.
 
[249]  Boström, J.; Norrby, P.-O.; Liljefors, T. Conformational energy penalties of protein-bound ligands. J. Comput.-Aided Mol. Des. ,1998, 12, 383-396.
 
[250]  Sitzmann, M.; Weidlich, I. E.; Filippov, I. V.; Liao, C.; Peach, M. L.; Ihlenfeldt, W.-D.; Karki, R. G.; Borodina, Y. V.; Cachau, R. E.; Nicklaus, M. C. PDB ligand conformational energies calculated quantum-mechanically. J. Chem. Inf. Model., 2012, 52, 739-756.
 
[251]  Ryde, U.; Olsen, L.; Nilsson, K. Quantum chemical geometry optimisations in proteins using crystallographic raw data. J. Comput.Chem., 2002, 23, 1058-1070.
 
[252]  Fu, Z.; Li, X.; Merz, K. M. Accurate assessment of the strain energy in a protein-bound drug using QM/MM X-ray refinement and converged quantum chemistry. J. Comput. Chem., 2011, 32, 2587-2597.
 
[253]  Genheden, S.; Ryde, U. How to obtain statistically converged MM/GBSA results. J. Comput. Chem., 2010, 31, 837-846.
 
[254]  Kolář, M.; Fanfrlík, J.; Hobza, P. Ligand conformational and solvation/desolvation free energy in protein-ligand complex formation. J. Phys. Chem. B, 2011, 115, 4718-4724.
 
[255]  Moghaddam, S.; Yang, C.; Rekharsky, M.; Ko, Y. H.; Kim, K.; Inoue, Y.; Gilson, M. K. 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., 2011, 133, 3570-3581.
 
[256]  Chen, W.; Chang, C.-E.; Gilson, M. K. Calculation of cyclodextrin binding affinities: energy, entropy, and implications for drug design. Biophys. J., 2004, 87, 3035-3049.
 
[257]  Zhou, H.-X.; Gilson, M. K. Theory of free energy and entropy in noncovalent binding. Chem. Rev., 2009, 109, 4092-4107.
 
[258]  Liu, L.; Guo, Q.-X. Isokinetic relationship, isoequilibrium relationship, and enthalpy−entropy compensation. Chem. Rev., 2001, 101, 673-696.
 
[259]  Sharp, K. Entropy−enthalpy compensation: fact or artifact? Protein Sci., 2001, 10, 661-667.
 
[260]  Chodera, J. D.; Mobley, D. L. Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. Annu. Rev. Biophys., 2013, 42, 121-142.
 
[261]  Ryde, U. A Fundamental view of enthalpy−entropy compensation. Med. Chem. Comm., 2014, 5, 1324-1336.
 
[262]  Moitessier, N.; Englebienne, P.; Corbeil, C. R. Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br. J. Pharmacol., 2008, 153, S7-S26.
 
[263]  Leach, A. R.; Shoichet, B. K.; Peishoff, C. E. Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem., 2006, 49, 5851-5855.
 
[264]  Cheng, T.; Li, X.; Wang, R. Comparative assessment of scoring functions on a diverse test set. J. Chem. Inf. Model., 2009, 49, 1079-1093.
 
[265]  Stjernschantz, E.; Marelius, J.; Oostenbrink, C. Are automated molecular dynamics simulations and binding free energy calculations realistic tools in lead optimization? An evaluation of the linear interaction energy (LIE) method. J. Chem. Inf. Model., 2006, 46, 1972-1983.
 
[266]  Claussen, H.; Buning, C.; Lengauer, T. FlexE: efficient molecular docking considering protein structure variations. J. Mol. Biol., 2001, 308, 377-395.
 
[267]  Sherman, W.; Day, T.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem., 2006, 49, 534-553.
 
[268]  Bottegoni, G.; Kufareva, I.; Abagyan, R. A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE). J. Comput. Aided Mol. Des., 2008, 22, 311-325.
 
[269]  Totrov, M.; Abagyan, R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr. Opin. Struct. Biol., 2008, 18, 178-184.
 
[270]  Verdonk, M. L.; Chessari, G.; Taylor, R. Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem., 2005, 48, 6504-6515.
 
[271]  Roberts, B. C.; Mancera, R. L. Ligand-protein docking with water molecules. J. Chem. Inf. Model., 2008, 48, 397-408.
 
[272]  Englebienne, P.; Moitessier, N. Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins? J. Chem. Inf. Model., 2009, 49, 1568-1580.
 
[273]  Vasanthanathan, P.; Hritz, J.; Oostenbrink, C. Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands. J. Chem. Inf. Model., 2009, 49, 43-52.
 
[274]  Santos, R.; Hritz, J.; Oostenbrink, C. Role of water in molecular docking simulations of cytochrome P450 2D6. J. Chem. Inf. Model., 2010, 50, 146-154.
 
[275]  Karplus, M.; McCammon, J. A. Molecular dynamics simulations of biomole cules. Nat. Struct. Biol., 2002, 9, 646-652.
 
[276]  Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov., 2004, 3, 935-949.
 
[277]  Lin, J. H.; Perryman, A. L.; Schames, J. R.; McCammon, J. A. Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J. Am. Chem. Soc., 2002, 124, 5632-5633.
 
[278]  Schames, J. R.; Henchman, R. H.; Siegel, J. S.; Sotriffer, C. A.; Ni, H.; McCammon, J. A. Discovery of a novel binding trench in HIV integrase. J. Med. Chem., 2004, 47, 1879-1881.
 
[279]  Boehm, H. J.; Boehringer, M.; Bur, D.; Gmuender, H.; Huber, W.; Klaus, W.; Kostrewa, D.; Kuehne, H.; Luebbers, T.; Meunier-Keller, N.; Mueller, F. Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J. Med. Chem., 2000, 43, 2664-2674.
 
[280]  Böhm, H. J.; Banner, D. W.; Weber, L. Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J. Comput. Aided Mol. Des., 1999, 13, 51-56.
 
[281]  Bressi, J. C.; Verlinde, C. L.; Aronov, A. M.; Shaw, M. L.; Shin, S. S.; Nguyen, L. N.; Suresh, S.; Buckner, F. S.; van Voorhis, W. C.; Kuntz, I. D.; Hol, W. G.; Gelb, M. H. Adenosine analogues as selective inhibitors of glyceraldehyde-3-phosphate dehydrogenase of Trypanosomatidae via structure-based drug design. J. Med. Chem., 2001, 44, 2080-2093.
 
[282]  Burkhard, P.; Hommel, U.; Sanner, M.; Walkinshaw, M. D. The discovery of steroids and other novel FKBP inhibitors using a molecular docking program. J. Mol. Biol., 1999, 287, 853-858.
 
[283]  Filikov, A. V.; Mohan, V.; Vickers, T. A.; Griffey, R. H.; Cook, P. D.; Abagyan, R. A.; James, T. L. Identification of ligands for RNA targets via structure-based virtual screening: HIV-1 TAR. J. Comput. Aided Mol. Des., 2000, 14, 593-610.
 
[284]  Iwata, Y.; Arisawa, M.; Hamada, R.; Kita, Y.; Mizutani, M. Y.; Tomioka, N.; Itai, A.; Miyamoto, S. Discovery of novel aldose reductase inhibitors using a protein structure-based approach: 3D-database search followed by design and synthesis. J. Med. Chem., 2001, 44, 1718-1728.
 
[285]  Schneider, G.; Clement-Chomienne, O.; Hilfiger, L.; Schneider, P.; Kirsch, S.;, Bohm, H, J.; Neidhart, W. Virtual screening for bioactive molecules by evolutionary de novo design. Angew. Chem. Int. Ed. Engl., 2000, 39, 4130-4133.
 
[286]  Schneider, G.;, Lee, M. L.; Stahl, M.; Schneider, P. De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J. Comput. Aided Mol. Des., 2000, 14, 487-494.
 
[287]  Kubinyi, H. Drug research: myths, hype and reality. Nat. Rev. Drug Discov., 2003, 2, 665-668.
 
[288]  Martin, Y. C. A bioavailability score. J. Med. Chem., 2005, 48, 3164-3170.
 
[289]  Blundell, T. L.; Jhoti, H.; Abell, C. High-throughput crystallography for lead discovery in drug design. Nat. Rev. Drug Discov., 2002, 1, 45-54.
 
[290]  Congreve, M.; Murray, C. W.; Blundell, T. L. Structural biology and drug discovery. Drug Discov. Today, 2005, 10, 895-907.
 
[291]  Thorsteinsdottir, H. B.; Schwede, T.; Zoete, V.; Meuwly, M. How innacuracies in protein structure models affect estimates of protein-ligands interactions: computational analysis of HIV-I protease inhibitor binding. Proteins Struct. Funct. Bioinf., 2006, 65, 407-423.
 
[292]  Paulsen, M. D.; Ornstein, R. L., Binding free energy calculations for P450cam-substrate complexes. Protein Eng. 1996, 9, 567-571.
 
[293]  Lewis, D. F. V.; Eddershaw, P. J.; Dickins, M.; Tarbit, M. H.; Goldfarb, P. S. Structural determinants of cytochrome P450 substrate specificity, binding affinity and catalytic rate. Chem.-Biol.Interact., 1998, 115, 175-199.
 
[294]  Lewis, D. F. V. Homology modelling of human cytochromes P450 involved in xenobiotic metabolism and rationalization of substrate selectivity. Exp. Toxic. Pathol., 1999, 51, 369-374.
 
[295]  Helms, V.; Wade, R. C. Computational alchemy to calculate absolute protein-ligand binding free energy. J. Am. Chem. Soc., 1998, 120, 2710-2713.
 
[296]  Plewczynski, D.; Łaźniewski, M.; Augustyniak, R.; Ginalski, K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comput. Chem., 2011, 32, 742-755.
 
[297]  Ramensky, V.; Sobol, A.; Zaitseva, N.; Rubinov, A.; Zosimov, V. A novel approach to local similarity of protein binding sites substantially improves computational drug design results. Proteins: Struct., Funct., Genet., 2007, 69, 349-357.
 
[298]  Muryshev, A. E.; Tarasov, D.; Butygin, A.; Butygina, O. Y.; Aleksandrov, A.; Nikitin, S. A novel scoring function for molecular docking. J. Comput. Aided Mol. Des., 2003, 17, 597-605.
 
[299]  Loving, K.; Alberts, I.; Sherman, W. Computational approaches for fragment-based and de novo design. Curr. Top. Med. Chem., 2010, 10, 14-32.
 
[300]  Hajduk, P. J.; Greer, J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat. Rev. Drug Discovery, 2007, 6, 211-219.
 
[301]  Erlanson, D. A.; McDowell, R. S.; O’Brien, T. Fragment-Based Drug Discovery. J. Med. Chem., 2004, 47, 3463-3482.
 
[302]  Carr, R. A. E.; Congreve, M.; Murray, C. W.; Rees, D. C. Fragment-based lead discovery: leads by design. Drug Discovery Today, 2005, 10, 987-992.
 
[303]  Murray, C. W.; Blundell, T. L. Structural biology in fragment based drug design. Curr. Opin. Struct. Biol., 2010, 20, 497-507.
 
[304]  Scott, D. E.; Coyne, A. G.; Hudson, S. A.; Abell, C. Fragment based approaches in drug discovery and chemical biology. Biochemistry, 2012, 51, 4990-5003.
 
[305]  Erlanson, D. Practical Fragments. http://practicalfragments.blogspot.de/2015/01/fragments-in-clinic-2015-edition.html (accessed 13.03.2016).
 
[306]  Congreve, M.; Carr, R.; Murray, C.; Jhoti, H. A ‘rule of three’for fragment-based lead discovery? Drug Discovery Today, 2003, 8, 876-877.
 
[307]  Lipinski, C. A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technol., 2004, 1, 337-341.
 
[308]  Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J. L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model., 2012, 52, 2864-75.
 
[309]  Baker, M. Fragment-based lead discovery grows up. Nat. Rev. Drug Discovery, 2013, 12, 5-7.
 
[310]  Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28, 849-857.
 
[311]  Lauri, G.; Bartlett, P. CAVEAT: A program to facilitate the design of organic molecules. J. Comput. Aided Mol. Des., 1994, 8, 51-66.
 
[312]  Eisen, M. B.; Wiley, D. C.; Karplus, M.; Hubbard, R. E. HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins. Struct. Funct. Genet., 1994, 19, 199-221.
 
[313]  Steinbrecher, T. B.; Dahlgren, M.; Cappel, D.; Lin, T.; Wang, L.; Krilov, G.; Abel, R.; Friesner, R.; Sherman, W. Accurate Binding Free Energy Predictions in Fragment Optimization. J. Chem. Inf. Model., 2015, 55, 2411-2420
 
[314]  Murray, C. W.; Erlanson, D. A.; Hopkins, A. L.; Keserü, G. M.; Leeson, P. D.; Rees, D. C.; Reynolds, C. H.; Richmond, N. J. Validity of ligand efficiency metrics. Med. Chem. Lett., 2014, 5, 616-618.
 
[315]  Shultz, M. D. Improving the plausibility of success with inefficient metrics. Med. Chem. Lett., 2014, 5, 2-5.
 
[316]  Wang, L.; Deng, Y.; Knight, J. L.; Wu, Y.; Kim, B.; Sherman, W.; Shelley, J. C.; Lin, T.; Abel, R. Modeling local structural rearrangements using FEP/REST: application to relative binding affinity predictions of CDK2 inhibitors. J. Chem. Theory Comput., 2013, 9, 1282-1293.
 
[317]  Wang, L.; Berne, B. J.; Friesner, R. A. On achieving high accuracy and reliability in the calculation of relative protein−ligand binding affinities. Proc. Natl. Acad. Sci. USA., 2012, 109, 1937-1942.
 
[318]  McCammon, J. A. Theory of biomolecular recognition. Curr. Opin. Struct. Biol., 1998, 8, 245-249.
 
[319]  Jorgensen, W. L. The many roles of computation in drug discovery. Science, 2004, 303, 1813-1818.
 
[320]  Hermans, J.; Wang, L. Inclusion of loss of translational and rotational freedom in theoretical estimates of free energies of binding. Application to a complex of benzene and mutant T4 lysozyme. J. Am. Chem. Soc., 1997, 119, 2707-2714.
 
[321]  Gilson, M. K.; Given, J. A.; Bush, B. L.; McCammon, J. A. The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys. J., 1997, 72, 1047-1069.
 
[322]  Boresch, S.; Tettinger, F.; Leitgeb, M. Absolute binding free energies: a quantitative approach to their calculation. J. Phys. Chem. B., 2003, 107, 9535-9551.
 
[323]  Jarzynski, C. Equilibrium free energy differences from nonequilibrium measurements: a master equation approach. Phys. Rev. E., 1997, 56, 5018.
 
[324]  Ytreberg, F. M.; Zuckerman, D. Efficient use of nonequilibrium measurement to estimate free energy differences for molecular systems. J. Comput. Chem., 2004, 25, 1749-1759.
 
[325]  Chang, C.E.; Gilson, M. K. Free energy, entropy, and induced fit in host-guest recognition: calculations with the second generation mining minima algorithm. J. Am. Chem. Soc., 2004, 126, 13156-13164.
 
[326]  Izrailev, S.; Stepaniants, S.; Balsera, M.; Oono, Y.; Schulten, K. Molecular dynamics study of unbinding of the avidin-biotin complex. Biophys. J., 1997, 72, 1568-1581.
 
[327]  Fukunishi, Y.; Mikami, Y.; Nakamura, H. The filling potential method: a method for estimating the free energy surface for protein-ligand docking. J. Phys. Chem. B., 2003, 107, 13201-13210.
 
[328]  Woo, H.J.; Roux, B. Calculation of absolute protein-ligand binding free energy from computer simulations. Proc. Natl. Acad. Sci. USA., 2005, 102, 6825-6830.
 
[329]  Srinivasan, J.; Cheatham, T. E. III; Cieplak, P.; Kollman, P. A.; Case, D. A. Continuum solvent studies of the stability of DNA, RNA, and Phosphoramidate-DNA helices. J. Am. Chem. Soc., 1998, 120, 9401-9409.
 
[330]  Gilson, M. K.; Rashin, A.; Fine, R.; Honig, B. On the calculation of electrostatic interactions in proteins. J. Mol. Biol., 1985, 184, 503-516.
 
[331]  Das, D.; Koh, Y.; Tojo,Y.; Ghosh, A. K.; Mitsuya, H. Prediction of potency of protease inhibitors using free energy simulations with polarizable quantum mechanics-based ligand charges and a hybrid water model. J. Chem. Inf. Model., 2009, 49, 2851-2862.
 
[332]  Meng, H.; Li, C.; Wang, Y.; Chen, G. Molecular dynamics simulation of the allosteric regulation of eIF4A protein from the open to closed state, induced by ATP and RNA substrates. PLoS One, 2014, 9, e86104.
 
[333]  Cheatham, T. E. 3rd; Srinivasan, J.; Case, D. A.; Kollman, P. A. Molecular dynamics and continuum solvent studies of the stability of polyG-polyC and polyA-polyT DNA duplexes in solution. J. Biomol. Struct. Dyn., 1998, 16, 265-280.
 
[334]  Ponder, J. W.; Wu, C. J.; Ren, P. Y.; Pande, V. S.; Chodera, J. D.; Schnieders, M. J.; Haque, I.; Mobley, D. L.; Lambrecht, D. S.; DiStasio, R. A. Jr.; Head-Gordon, M.; Clark, G. N. I.; Johnson, M. E.; Head-Gordon, T. Current status of the AMOEBA polarizable force field. J. Phys. Chem. B, 2010, 114, 2549-2564.
 
[335]  Lopes, P. E. M.; Huang, J.; Shim, J.; Luo, Y.; Li, H.; Roux, B.; MacKerell, A. D., Jr. Polarizable force field for peptides and proteins based on the classical drude oscillator. J. Chem. Theory Comput., 2013, 9, 5430-5449.
 
[336]  Banks, J. L.; Kaminski, G. A.; Zhou, R. H.; Mainz, D. T.; Berne, B. J.; Friesner, R. A. Parametrizing a polarizable force field from Ab Initio data. I. The fluctuating point charge model. J. Chem. Phys., 1999, 110, 741-754.
 
[337]  Gresh, N.; Cisneros, G. A.; Darden, T. A.; Piquemal, J. P. Anisotropic, polarizable molecular mechanics studies of inter- and intramoecular interactions and ligand-macromolecule complexes. A bottom-up strategy. J. Chem. Theory Comput., 2007, 3, 1960-1986.
 
[338]  Schmidt, J. R.; Yu, K.; McDaniel, J. G. Transferable next-generation force fields from simple liquids to complex materials. Acc. Chem. Res., 2015, 48, 548-556.
 
[339]  Rotenberg, B.; Salanne, M.; Simon, C.; Vuilleumier, R. From localized orbitals to material properties: building classical force fields for nonmetallic condensed matter systems. Phys. Rev. Lett., 2010, 104, 138301.
 
[340]  Grimme, S. A. General quantum mechanically derived force field (QMDFF) for molecules and condensed phase simulations. J. Chem. Theory Comput., 2014, 10, 4497-4514.
 
[341]  Weinhold, F.; Landis, C. R. Valency and Bonding. Cambridge University Press, Cambridge, UK, 2005.
 
[342]  Medders, G. R.; Paesani, F. On the interplay of the potential energy and dipole moment surfaces in controlling the infrared activity of liquid water. J. Chem. Phys., 2015, 142, 212411.
 
[343]  Leontyev, I. V.; Stuchebrukhov, A. A. Accounting for Electronic Polarization in Non-Polarizable Force Fields. Phys. Chem. Chem. Phys., 2011, 13, 2613-2626.
 
[344]  Reddy, M. R.; Erion, M. D. Relative binding affinities of fructose-1,6-bisphosphatase inhibitors calculated using a quantum mechanics-based free energy perturbation method. J. Am. Chem. Soc., 2007, 129, 9296-9297.
 
[345]  Rathore, R. S.; Reddy, R. N.; Kondapi, A. K.; Reddanna, P.; Reddy, M. R. Use of quantum mechanics/molecular mechanics-based FEP method for calculating relative binding affinities of FBPase inhibitors for type-2 diabetes. Theor. Chem. Acc., 2012, 131, 1096.
 
[346]  Rod, T. H.; Ryde, U. Quantum mechanical free energy barrier for an enzymatic reaction. Phys. Rev. Lett., 2005, 94, 138302.
 
[347]  Luzhkov, V.; Warshel, A. Microscopic models for quantum mechanical calculations of chemical processes in solutions: LD/AMPAC and SCAAS/AMPAC calculations of solvation energies. J. Comput. Chem., 1992, 13, 199-213.
 
[348]  Duarte, F.; Amrein, B. A.; Blaha-Nelson, D.; Kamerlin, S. C. L. Recent advances in QM/mm free energy calculations using reference potentials. Biochim. Biophys. Acta, Gen. Subj., 2015, 1850, 954-965.
 
[349]  König, G.; Boresch, S. Non-Boltzmann sampling and Bennett’s acceptance ratio method: how to profit from bending the rules. J. Comput. Chem., 2011, 32, 1082-1090.
 
[350]  Senn, H. M.; Thiel, W. QM/MM methods for biomolecular systems. Angew. Chem., Int. Ed., 2009, 48, 1198-1229.
 
[351]  Hu, H.; Yang, W. Free energies of chemical reactions in solution and in enzymes with Ab Initio quantum mechanics/molecular mechanics methods. Annu. Rev. Phys. Chem., 2008, 59, 573-601.
 
[352]  Heimdal, J.; Ryde, U. Convergence of QM/MM free-energy perturbations based on molecular-mechanics or semiempirical simulations. Phys. Chem. Chem. Phys., 2012, 14, 12592-12604.
 
[353]  Woods, C. J.; Manby, F. R.; Mulholland, A. J. An efficient method for the calculation of quantum mechanics/molecular mechanics free energies. J. Chem. Phys., 2008, 128, 014109.
 
[354]  Fox, S. J.; Pittock, C.; Tautermann, C. S.; Fox, T.; Christ, C.; Malcolm, N. O. J.; Essex, J. W.; Skylaris, C.-K. Free energies of binding from large-scale first-principles quantum mechanical calculations: application to ligand hydration energies. J. Phys. Chem. B, 2013, 117, 9478-9485.
 
[355]  Genheden, S.; Cabedo Martinez, A. I.; Criddle, M. P.; Essex, J. W. Extensive all-atom Monte Carlo sampling and QM/MM corrections in the SAMPL4 hydration free energy challenge. J. Comput. Aided Mol. Des., 2014, 28, 187-200.
 
[356]  König, G.; Pickard, F. C.; Mei, Y.; Brooks, B. R. Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4. J. Comput. Aided Mol. Des., 2014, 28, 245-257.
 
[357]  König, G.; Hudson, P. S.; Boresch, S.; Woodcock, H. L. Multiscale free energy simulations: an efficient method for connecting classical MD simulations to QM or QM/MM free energies using non-Boltzmann Bennett reweighting schemes. J. Chem. Theory Comput., 2014, 10, 1406-1419.
 
[358]  Sampson, C.; Fox, T.; Tautermann, C. S.; Woods, C.; Skylaris, C.-K. A “Stepping stone” approach for obtaining quantum free energies of hydration. J. Phys. Chem. B, 2015, 119, 7030-7040.
 
[359]  Mikulskis, P.; Cioloboc, D.; Andrejic, M.; Khare, S.; Brorsson, J.; Genheden, S.; Mata, R. A.; Söderhjelm, P.; Ryde, U. Free-energy perturbation and quantum mechanical study of SAMPL4 octa-acid host-guest binding energies. J. Comput. Aided Mol. Des., 2014, 28, 375-400.
 
[360]  Beierlein, F. R.; Michel, J.; Essex, J. W. A simple QM/MM approach for capturing polarization effects in protein ligand binding free energy calculations. J. Phys. Chem. B, 2011, 115, 4911-4926.
 
[361]  Woods, C. J.; Shaw, K. E.; Mulholland, A. J. Combined quantum mechanics/molecular mechanics (QM/MM) simulations for protein−ligand complexes: free energies of binding of water molecules in influenza neuraminidase. J. Phys. Chem. B, 2015, 119, 997-1001.
 
[362]  Genheden, S.; Ryde, U.; Söderhjelm, P. Binding affinities by free-energy perturbation using QM/MM with a large QM system and polarizable MM model. J. Comput. Chem., 2015, 36, 2114-2124.
 
[363]  Olsson, M. A.; Söderhjelm, P.; Ryde, U. Converging ligand-binding free energies obtained with free-energy perturbations at the quantum mechanical level. J. Comput. Chem., 2016.
 
[364]  Hansen, N.; van Gunsteren, W. F. Practical aspects of free-energy calculations: a review. J. Chem. Theory Comput., 2014, 10, 2632-2647.
 
[365]  Mobley, D. L.; Klimovich, P. V. Perspective: alchemical free energy calculations for drug discovery. J. Chem. Phys., 2012, 137, 230901.
 
[366]  Aqvist, J.; Hansson, T. On the validity of electrostatic linear response in polar solvents. J. Phys. Chem., 1996, 100, 9512-9521.
 
[367]  Almlöf, M.; Carlsson, J.; Åqvist, J. Improving the accuracy of the linear interaction energy method for solvation free energies. J. Chem. Theory Comput., 2007, 3, 2162-2175.
 
[368]  Korth, M.; Lüchow, A.; Grimme, S. Toward the exact solution of the electronic schrödinger equation for noncovalent molecular interactions: worldwide distributed quantum Monte Carlo calculations. J. Phys. Chem. A, 2008, 112, 2104-2109.
 
[369]  Tkatchenko, A.; Alfè, D.; Kim, K. S. First-principles modeling of non-covalent interactions in supramolecular systems: the role of many-body effects. J. Chem. Theory Comput., 2012, 8, 4317-4322.
 
[370]  Hongo, K.; Cuong, N. T.; Maezono, R. The importance of electron correlation on stacking interaction of adenine-thymine base-pair step in βDNA: a quantum monte carlo study. J. Chem. Theory Comput., 2013, 9, 1081-1086.
 
[371]  Benali, A.; Shulenburger, L.; Romero, N. A.; Kim, J.; von Lilienfeld, O. A. Application of diffusion monte carlo to materials dominated by van der Waals interactions. J. Chem. Theory Comput., 2014, 10, 3417-3422.
 
[372]  Dubecký, M.; Jurečka, P.; Derian, R.; Hobza, P.; Otyepka, M.; Mitas, L. Quantum Monte Carlo methods describe noncovalent interactions with subchemical accuracy. J. Chem. Theory Comput., 2013, 9, 4287-4292.
 
[373]  Al-Hamdani, Y. S.; Alfè, D.; von Lilienfeld, O. A.; Michaelides, A. Water on BN doped benzene: a hard test for exchange-correlation functionals and the impact of exact exchange on weak binding. J. Chem. Phys., 2014, 141, 18C530.
 
[374]  Gillan, M. J.; Manby, F. R.; Towler, M. D.; Alfè, D. Assessing the accuracy of quantum Monte Carlo and density functional theory for energetics of small water clusters. J. Chem. Phys., 2012, 136, 244105.
 
[375]  Gurtubay, I. G.; Needs, R. J. Dissociation energy of the water dimer from quantum Monte Carlo calculations. J. Chem. Phys., 2007, 127, 124306.
 
[376]  Santra, B.; Michaelides, A.; Fuchs, M.; Tkatchenko, A.; Filippi, C.; Scheffler, M. On the accuracy of density-functional theory exchange correlation functionals for H bonds in small water clusters. II. The water hexamer and van der Waals interactions. J. Chem. Phys., 2008, 129, 194111.
 
[377]  Ma, J.; Alfè, D.; Michaelides, A.; Wang, E. The water-benzene interaction: insight from electronic structure theories. J. Chem. Phys., 2009, 130, 154303.
 
[378]  Korth, M.; Grimme, S.; Towler, M. D. The lithium-thiophene riddle revisited. J. Phys. Chem. A, 2011, 115, 11734-11739.
 
[379]  Řezáč, J.; Dubecký, M.; Jurečka, P.; Hobza, P. Extensions and applications of the A24 data set of accurate interaction energies. Phys. Chem. Chem. Phys., 2015, 17, 19268-19277.
 
[380]  Xu, J.; Deible, M. J.; Peterson, K. A.; Jordan, K. D. Correlation consistent Gaussian basis sets for H, B-Ne with Dirac-Fock AREP pseudopotentials: applications in Quantum Monte Carlo calculations. J. Chem. Theory Comput., 2013, 9, 2170-2178.
 
[381]  Petruzielo, F. R.; Toulouse, J.; Umrigar, C. J. Approaching chemical accuracy with quantum Monte Carlo. J. Chem. Phys., 2012, 136.
 
[382]  Manten, S.; Lüchow, A. On the accuracy of the fixed-node diffusion quantum Monte Carlo method. J. Chem. Phys., 2001, 115, 5362-5366.
 
[383]  Grossman, J. C. Benchmark quantum Monte Carlo calculations. J. Chem. Phys., 2002, 117, 1434-1440.
 
[384]  Nemec, N.; Towler, M. D.; Needs, R. J. Benchmark all-electron ab initio quantum Monte Carlo calculations for small molecules. J. Chem. Phys., 2010, 132, 034111.
 
[385]  Morales, M. A.; McMinis, J.; Clark, B. K.; Kim, J.; Scuseria, G. E. Multideterminant wave functions in quantum Monte Carlo. J. Chem. Theory Comput., 2012, 8, 2181-2188.
 
[386]  Mazziotti, D. A. Parametrization of the two-electron reduced density matrix for its direct calculation without the many-electron wave function. Phys. Rev. Lett., 2008, 101, No. 253002.
 
[387]  DePrince, A. E.; Kamarchik, E.; Mazziotti, D. A. Parametric two electron reduced-density-matrix method applied to computing molecular energies and properties at nonequilibrium geometries. J. Chem. Phys., 2008, 128, 234103.
 
[388]  Mazziotti, D. A. Two-electron reduced density matrix as the basic variable in many-electron quantum chemistry and physics. Chem. Rev., 2012, 112, 244-262.
 
[389]  Chan, G. K.-L.; Head-Gordon, M. Highly correlated calculations with a polynomial cost algorithm: a study of the density matrix renormalization group. J. Chem. Phys., 2002, 116, 4462.
 
[390]  Chan, G. K.-L. An algorithm for large scale density matrix renormalization group calculations. J. Chem. Phys., 2004, 120, 3172.
 
[391]  Sharma, S.; Chan, G. K.-L. Spin-adapted density matrix renormalization group algorithms for quantum chemistry. J. Chem. Phys., 2012, 136, 124121.
 
[392]  Rasch, K. M.; Hu, S.; Mitas, L. M. Fixed-node errors in quantum Monte Carlo: interplay of electron density and node nonlinearities. J. Chem. Phys., 2014, 140, 041102.
 
[393]  Yang, W. T. Direct calculation of electronic density in density functional theory. Phys. Rev. Lett., 1991, 66, 1438-1441.
 
[394]  Goedecker, S. Linear scaling electronic structure methods. Rev. Mod. Phys., 1999, 71, 1085-1123.
 
[395]  Bowler, D. R.; Miyazaki, T. O(N) Methods in electronic structure calculations. Rep. Prog. Phys., 2012, 75, 036503.
 
[396]  Karplus, M.; Kuriyan, J. Molecular dynamics and protein function. Proc. Natl. Acad. Sci. USA., 2005, 102, 6679-6685.
 
[397]  Dror, R. O.; Dirks, R. M.; Grossman, J. P.; Xu, H. F.; Shaw, D. E. Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys., 2012, 41, 429-452.
 
[398]  Mikulskis, P.; Genheden, S.; Wichmann, K.; Ryde, U. A semiempirical approach to ligand-binding affinities: dependence on the hamiltonian and corrections. J. Comput. Chem., 2012, 33, 1179-1189.
 
[399]  Söderhjelm, P.; Kongsted, J.; Ryde, U. Ligand affinities estimated by quantum chemical calculations. J. Chem. Theory Comput., 2010, 6, 1726-1737.
 
[400]  Smith, R. Peer review: a flawed process at the heart of science and journals. J. R. Soc. Med., 2006, 99, 178-182.
 
[401]  Emerson, G. B.; Warme, W. J.; Wolf, F. M.; Heckman, J. D.; Brand, R. A.; Leopold, S. S. Testing for the presence of positive-outcome bias in peer review. a randomized controlled trial. Arch. Intern. Med., 2010, 170, 1934-1939.
 
[402]  Pople, J. A.; Beveridge, D. L. Approximate Molecular Orbital Theory. McGraw-Hill, New York, 1970.
 
[403]  Dewar, M. J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P. Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc., 1985, 107, 3902-3909.
 
[404]  Stewart, J. J. P. Optimization of parameters for semiempirical methods II. Applications. J. Comput. Chem., 1989, 10, 221-264.
 
[405]  Thiel, W.; Voityuk, A. A. Extension of MNDO to d-orbitals parameters and results for the second-row elements and for the zinc group. J. Phys. Chem., 1996, 100, 616-626.
 
[406]  Weber, W.; Thiel, W. Orthogonalization corrections for semiempirical methods. Theor. Chem. Acc., 2000, 103, 495-506.
 
[407]  Elstner, M.; Frauenheim, T.; Kaxiras, E.; Seifert, G.; Suhai, S. A self-consistent charge density-functional based tight-binding scheme for large biomolecules. Phys. Status Solidi B, 2000, 217, 357-376.
 
[408]  Elstner, M.; Seifert, G. Density functional tight binding. Philos. Trans. R. Soc. A, 2014, 372, 20120483.
 
[409]  Gaus, M.; Cui, Q.; Elstner, M. Density functional tight binding: application to organic and biological molecules. WIREs Comput. Mol. Sci., 2014, 4, 49-61.
 
[410]  Cui, Q.; Elstner, M. Density functional tight binding: values of semi-empirical methods in an ab initio era. Phys. Chem. Chem. Phys., 2014, 16, 14368-14377.
 
[411]  Maia, J. D. C.; Urquiza Carvalho, G. A.; Mangueira, C. P., Jr.; Santana, S. R.; Cabral, L. A. F.; Rocha, G. B. GPU linear algebra libraries and GPGPU programming for accelerating MOPAC semiempirical quantum chemistry calculations. J. Chem. Theory Comput., 2012, 8, 3072-3081.
 
[412]  Wu, X.; Koslowski, A.; Thiel, W. Semiempirical quantum chemical calculations accelerated on a hybrid multicore CPU-GPU computing platform. J. Chem. Theory Comput., 2012, 8, 2272-2281.
 
[413]  Gao, J. L.; Truhlar, D. G.; Wang, Y. J.; Mazack, M. J. M.; Loffler, P.; Provorse, M. R.; Rehak, P. Explicit polarization: a quantum mechanical framework for developing next generation force fields. Acc. Chem. Res., 2014, 47, 2837-2845.
 
[414]  Giese, T. J.; Huang, M.; Chen, H. Y.; York, D. M. Recent advances toward a general purpose linear-scaling quantum force field. Acc. Chem. Res., 2014, 47, 2812-2820.
 
[415]  Niklasson, A. M. N.; Cawkwell, M. J. Generalized extended lagrangian Born-Oppenheimer molecular dynamics. J. Chem. Phys., 2014, 141, 164123.
 
[416]  Thiel, W. Perspectives on semiempirical molecular orbital theory. Adv. Chem. Phys., 1996, 93, 703-757.
 
[417]  Sattelmeyer, K. W.; Tirado-Rives, J.; Jorgensen, W. Comparison of SCC-DFTB and NDDO-based semiempirical molecular orbital methods for organic molecules. J. Phys. Chem. A, 2006, 110, 13551-13559.
 
[418]  Otte, N.; Scholten, M.; Thiel, W. Looking at self-consistent-charge density functional tight binding from a semiempirical perspective. J. Phys. Chem. A, 2007, 111, 5751-5755.
 
[419]  Kruger, T.; Elstner, M.; Schiffels, P.; Frauenheim, T. Validation of the density functional based tight-binding approximation method for the calculation of reaction energies and other data. J. Chem. Phys., 2005, 122, 114110.
 
[420]  Riley, K. E.; Hobza, P. Investigations into the nature of halogen bonding including symmetry adapted perturbation theory analyses. J. Chem. Theory Comput., 2008, 4, 232-242.
 
[421]  Řezáč, J.; Hobza, P. A Halogen-bonding correction for the semiempirical PM6 method. Chem. Phys. Lett., 2011, 506, 286-289.
 
[422]  Császár, A. G.; Allen, W. D.; Schaefer, H. F. In pursuit of the ab initio limit for conformational energy prototypes. J. Chem. Phys., 1998, 108, 9751-9764.
 
[423]  Halkier, A.; Helgaker, T.; Jørgensen, P.; Klopper, W.; Koch, H.; Olsen, J.; Wilson, A. K. Basis-set convergence in correlated calculations on Ne, N2, and H2O. Chem. Phys. Lett., 1998, 286, 243-252.
 
[424]  Boys, S.; Bernardi, F. The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors. Mol. Phys., 1970, 19, 553-566.
 
[425]  Řezáč, J.; Riley, K. E.; Hobza, P. Extensions of the S66 data set: more accurate interaction energies and angular-displaced nonequilibrium geometries. J. Chem. Theory Comput., 2011, 7, 3466-3470.
 
[426]  Řezáč, J.; Hobza, P. Describing noncovalent interactions beyond the common approximations: how accurate is the ”gold standard,” CCSD(T) at the complete basis set limit? J. Chem. Theory Comput., 2013, 9, 2151-2155.
 
[427]  Christensen, A. S.; Elstner, M.; Cui, Q. Improving intermolecular interactions in DFTB3 using extended polarization from chemical-potential equalization. J. Chem. Phys., 2015, 143, 084123.
 
[428]  Liakos, D. G.; Sparta, M.; Kesharwani, M. K.; Martin, J. M. L.; Neese, F. Exploring the accuracy limits of local pair natural orbital coupled-cluster theory. J. Chem. Theory Comput., 2015, 11, 1525-1539.
 
[429]  Bissantz, C.; Kuhn, B.; Stahl, M. A medicinal chemist’s guide to molecular interactions. J. Med. Chem., 2010, 53, 5061-5084.
 
[430]  Guerra, C. F.; Bickelhaupt, F. M.; Snijders, J. G.; Baerends, E. J. The nature of the hydrogen bond in DNA base pairs: the role of charge transfer and resonance assistance. Chem. Eur. J., 1999, 5, 3581-3594.
 
[431]  Wolters, L. P.; Bickelhaupt, F. M. Halogen bonding versus hydrogen bonding: a molecular orbital perspective. Chemistry Open, 2012, 1, 96-105.
 
[432]  Dixon, S. L.; Merz, K. M., Jr. semiempirical molecular orbital calculations with linear system size scaling. J. Chem. Phys., 1996, 104, 6643-6649.
 
[433]  Lepšík, M.; Řezáč, J.; Kolář, M.; Pecina, A.; Hobza, P.; Fanfrlík, J. The semiempirical quantum mechanical scoring function for in silico drug design. ChemPlusChem, 2013, 78, 921-931.
 
[434]  Mucs, D.; Bryce, R. A. The application of quantum mechanics in structure-based drug design. Expert Opin. Drug Discovery, 2013, 8, 263-276.
 
[435]  Yilmazer, N. D.; Korth, M. Comparison of molecular mechanics, semi-empirical quantum mechanical, and density functional theory methods for scoring protein-ligand interactions. J. Phys. Chem. B, 2013, 117, 8075-8084
 
[436]  Nikitina, E.; Sulimov, V.; Zayets, V.; Zaitseva, N. Semiempirical calculations of binding enthalpy for protein-ligand complexes. Int. J. Quantum Chem., 2004, 97, 747-763.
 
[437]  Bikadi, Z.; Hazai, E. Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. J. Cheminf., 2009, 1, 15.
 
[438]  Fanfrlík, J.; Bronowska, A. K.; Řezáč, J.; Přenosil, O.; Konvalinka, J.; Hobza, P. A Reliable docking/scoring scheme based on the semiempirical quantum mechanical PM6-DH2 method accurately covering dispersion and H-bonding: HIV-1 Protease with 22 ligands. J. Phys. Chem. B, 2010, 114, 12666-12678.
 
[439]  Peters, M. B.; Raha, K.; Merz, K. M., Jr. Quantum mechanics in structure-based drug design. Curr. Opin. Drug Discovery Dev., 2006, 9, 370-379.
 
[440]  Raha, K.; Peters, M. B.; Wang, B.; Yu, N.; Wollacott, A. M.; Westerhoff, L. M.; Merz, K. M., Jr. The role of quantum mechanics in structure-based drug design. Drug Discovery Today, 2007, 12, 725-731.
 
[441]  Cattoni, D. I.; Chara, O.; Kaufman, S. B.; González Flecha, F. L. Cooperativity in binding processes: new insights from phenomenological modeling. PLoS one, 2013, 10, e0146043.
 
[442]  Garbett, N. C.; Chaires, J. B. Binding: a polemic and rough guide. Methods Cell Biol., 2008, 84, 3-23.
 
[443]  Gutfreund, H. Kinetics for the life sciences: Receptors, transmitters and catalysts. Cambridge University Press, Cambridge, 1995.
 
[444]  Hager, G. L.; McNally, J. G.; Misteli, T. Transcription dynamics. Mol. Cell., 2009, 35, 741-753.
 
[445]  Freiburger, L.; Miletti, T.; Zhu, S.; Baettig, O.; Berghuis, A.; Auclair, K.; Mittermaier, A,. Substrate-dependent switching of the allosteric binding mechanism of a dimeric enzyme. Nat Chem Biol., 2014; 10: 937-942.
 
[446]  Dodes Traian, M. M.; Cattoni, D. I.; Levi, V.; González Flecha, F. L. A two-stage model for lipid modulation of the activity of integral membrane proteins. PLoS one, 2010, 7, e39255.
 
[447]  Hillier, B. J.; Rodriguez, H. M.; Gregoret, L. M. Coupling protein stability and protein function in Escherichia coli CspA. Fold Des., 1998, 3, 87-93.
 
[448]  Levi, V.; Rossi, J, P. F. C.; Castello, P. R.; González Flecha, F. L. Structural significance of the plasma membrane calcium pump oligomerization. Biophys. J., 2002, 82, 437-446.
 
[449]  Shcheynikov, N.; Son, A.; Hong, J. H.; Yamazaki, O.; Ohana, E.; Kurtz, I.; Shin, D. M.; Muallem, S. Intracellular Cl- as a signaling ion that potently regulates Na+/HCO3- transporters. Proc. Natl. Acad. Sci. USA, 2015A, 112, E329-337.
 
[450]  Ruscio, J. Z.; Kumar, D.; Shukla, M.; Prisant, M. G.; Murali, T. M.; Onufriev, A. V. Atomic level computational identification of ligand migration pathways between solvent and binding site in myoglobin. Proc. Natl. Acad. Sci. USA., 2008, 105, 9204-9209.
 
[451]  Sakaguchi, M.; Shingo, T.; Shimazaki, T.; Okano, H. J.; Shiwa, M.; Ishibashi, S.; Oguro, H.; Ninomiya, M.; Kadoya, T.; Horie, H.; Shibuya, A.; Mizusawa, H.; Poirier, F.; Nakauchi, H.; Sawamoto, K.; Okano, H. A carbohydrate-binding protein, Galectin-1, promotes proliferation of adult neural stem cells. Proc. Natl. Acad. Sci. USA., 2006, 103, 7112-7117.
 
[452]  Edelstein, S. J.; Le Novere, N. Cooperativity of allosteric receptors. J. Mol. Biol., 2013, 425, 1424-1432.
 
[453]  Barcroft, J.; Hill, A.V. The nature of oxyhæmoglobin, with a note on its molecular weight. J. Physiol., 1910, 39, 411-428.
 
[454]  Hill, A.V. The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J. Physiol., 1910, 40, iv-vii
 
[455]  Holt, J. M.; Ackers, G. K. The Hill coefficient. Inadequate resolution of cooperativity in human hemoglobin. Methods Enzymol., 2009, 455, 193-212.
 
[456]  Weber, G. Protein interactions. Chapman and Hall, New York, 1992.
 
[457]  Ruzicka, F. J.; Frey, P. A. Kinetic and spectroscopic evidence of negative cooperativity in the action of lysine 2,3-aminomutase. J. Phys. Chem. B, 2010, 114, 16118-16124.
 
[458]  Abeliovich, H. An empirical extremum principle for the Hill coefficient in ligand-protein interactions showing negative cooperativity. Biophys. J., 2005, 89, 76-79.
 
[459]  Koshland, D. E.; Hamadani, K. Proteomics and models for enzyme cooperativity. J. Biol. Chem., 2002, 277, 46841-46844.
 
[460]  Thorsteinsdottir, H. B. Computational analysis of protein-ligand binding: from single continuous trajectories to multiple parallel simulations. Ph. D. Disertation, Basel University, 2010.
 
[461]  Deng, Y.; Roux, B. Computations of standard binding free energies with molecular dynamics simulations. J. Phys. Chem., 2009, 113, 2234-2246.
 
[462]  Gilson, M. K.; Zhou, H. X. Calculation of protein-ligand binding affinities. Ann. Rev. Biophys. Biomol. Struct., 2007, 36, 21-42.
 
[463]  Kollman, P. Free energy calculations: Applications to chemical and biochemical phenomena. Chem. Rev., 1993, 93, 2395-2417.
 
[464]  Kirkwood, J. G. Statistical mechanics of fluid mixtures. J. Chem. Phys., 1935, 3, 300-313.
 
[465]  Zwanzig, R. W. High-temperature equation of state by a perturbation method. I. Nonpolar gases. J. Chem. Phys., 1954, 22, 1420-1426.
 
[466]  Cornell, W.D.; Cieplak, P.; Bayly C. I.; Gould I. R.; Merz K. M.; Ferguson D. M.; Spellmeyer D. C.; Fox T.; Caldwell J. W.; Kollman P. A. A second generation forcefield for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc., 1995,117, 5179-5197.
 
[467]  MacKerell, A.D.; Bashford, D.; Bellott M.; Dunbrack, Jr., R. L.; Evanseck, J. D.; Field, M. J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F.T.K.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D. T.; Prodhorn, B.; Reiher, III, W. E.; Roux, B.; Schlenkrich, M.; Smith, J. C.; Stote, R.; Straub, J.; Watanabe M.; Wiórkiewicz-Kuczera, J.; Yin, D.; Karplus, M. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B, 1998, 102, 3586-3616.
 
[468]  Van Gunsteren, W. F.; Billeter, S. R.; Eising, A. A.; Hünenberger, P. H.; Krueger, P.; Mark, A. E.; Scott, W. R. P.; Tironi, I. G. Biomolecular Simulation: The GROMOS96 manual and user guide, 1996, vdf Hochschulverlag AG an der ETH Zürich, and BIOMOS b.v. Zürich, Groningen.
 
[469]  Bowers, K. J.; Chow, E.; Xu, H.; Dror, R. O.; Eastwood, M. P.; Gregersen, B. A.; Klepeis, J. L.; Kolossvary, I.; Moraes, M. A.; Sacerdoti, F. D.; Salmon, J. K.; Shan, Y.; Shaw, D. E. Scalable algorithms for molecular dynamics simulations on commodity clusters, Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), 2006, Tampa, Florida, USA.
 
[470]  Lee, M. S.; Olson, M. A. Calculation of absolute protein-ligand binding affinity using path and endpoint approaches. Biophys. J., 2006, 90, 864-877.
 
[471]  Mobley, D. L.; Graves, A. P.; Chodera, J. D.; McReynolds, A. C.; Shoichet, B. K.; Dill, K. A. Predicting absolute binding free energies to a simple model site. J. Mol.Biol., 2007, 371, 1118-1134.
 
[472]  Tembe, B.; McCammon, J. A. Ligand-receptor interactions. Comput. Chem., 1984, 8, 281-283.
 
[473]  Rao, B. G.; Kim, E. E.; Murcko, M. A. Calculation of solvation and binding free energy differences between VX-478 and its analogs by free energy perturbation and Amsol methods. J. Comput. Aided. Mol. Des., 1996, 10, 23-30.
 
[474]  Oostenbrink, C.; van Gunsteren, W. F. Free energies of binding of polychlorinated biphenyls to the estrogen receptor from a single simulation. Proteins, 2004, 54, 237-46.
 
[475]  Lu, N.; Woolf, T. B. Chapter 6: Understanding and improving free energy calculations in molecular simulations: Error analysis and reduction methods. In: Free Energy Calculations: Theory & Applications in Chemistry & Biology (Springer Series in Chemical Physics Vol.86, 2007, Chipot, C.; Pohorille, A. Eds. Springer-Verlag, Berlin-Heidelberg.
 
[476]  Chodera, J. D.; Mobley, D. L.; Shirts, M. R.; Dixon, R. W., Branson K.; Pande V.S. Alchemical free energy methods for drug discovery: progress and challenges. Curr. Opin. Struct. Biol., 2011, 21, 150-160.
 
[477]  Singh, N.; Warshel, A. Absolute binding free energy calculations: on the accuracy of computational scoring of protein-ligand interactions. Prot. Struct., Funct. Bioinf., 2010, 78, 1705-1723.
 
[478]  Hayes, J. M.; Skamnaki, V. T.; Archontis, G.; Lamprakis, C.; Sarrou, J.; Bischler, N.; Skaltsounis, A. L.; Zographos, S. E.; Oikonomakos, N. G. Kinetics, in silico docking, molecular dynamics, and MM-GBSA binding studies on prototype indirubins, KT5720, and staurosporine as phosphorylase kinase ATP-binding site inhibitors: The role of water molecules examined. Prot. Struct. Funct. Bioinf., 2011, 79, 703-719.
 
[479]  Archontis, G.; Simonson, T. Proton binding to proteins: a free-energy component analysis using a dielectric continuum model. Biophys. J., 2005, 88, 3888-3904.
 
[480]  Schutz, C. N.; Warshel, A. What are the dielectric constants of proteins and how to validate electrostatic models? Prot. Struct. Funct. Genet., 2001, 44, 400-417.
 
[481]  Aleksandrov, A.; Thompson, D.; Simonson, T. Alchemical free energy simulations for biological complexes: powerful but temperamental. J. Mol. Recog., 2010, 23, 117-127.
 
[482]  Archontis, G.; Simonson T.; Karplus M. Binding free energies and free energy components from molecular dynamics and Poisson-Boltzmann calculations. Application to amino acid recognition by aspartyl-tRNA synthetase. J. Mol. Biol., 2001, 306, 307-327.
 
[483]  Thompson, D.; Plateau, P.; Simonson, T. Free-energy simulations and experiments reveal long-range electrostatic interactions and substrate-assisted specificity in an aminoacyl-tRNAsynthetase. Chembiochem., 2006, 7, 337-344.
 
[484]  Pearlman, D. A. Evaluating the molecular mechanics Poisson-Boltzmann surface area free energy method using a congeneric series of ligands to p38 MAP kinase. J. Med. Chem., 2005, 48, 7796-7807.
 
[485]  Brooks, B. R.; Janezic, D.; Karplus, M. Harmonic-analysis of large systems. 1. Methodology. J. Comput. Chem., 1995, 16, 1522-1542.
 
[486]  Karplus, M.; Kushick, J. N. Method for estimating the configurational entropy of macromolecules. Macromol., 1981, 14, 325-332.
 
[487]  Tidor, B.; Karplus, M. The contribution of vibrational entropy to molecular association - the dimerization of insulin. J. Mol. Biol., 1994, 238, 405-414.
 
[488]  Kongsted, J.; Ryde, U. An improved method to predict the entropy term with the MM/PBSA approach. J. Comp. Aid. Mol. Des., 2009, 23, 63-71.
 
[489]  Gohlke, H.; Case, D. Converging free energy estimates: MM-PB(GB)SA studies on the protein-protein complex Ras-Raf. J. Comput. Chem., 2004, 25, 238-250.
 
[490]  Page, C. S.; Bates, P. A. Can MM-PBSA calculations predict the specificities of protein kinase inhibitors? J. Comput. Chem., 2006, 27, 1990-2007.
 
[491]  Simonson, T. Electrostatics and dynamics of proteins. Rep. Prog. Phys., 2003, 66, 737-787.
 
[492]  Tamamis, P.; Morikis, D.; Floudas, C. A.; Archontis, G. Species specificity of the complement inhibitor compstatin investigated by all-atom molecular dynamics simulations. Prot. Struct., Funct. Bioinf., 2010, 78, 2655-2667.
 
[493]  Yang, C-Y.; Sun, H.; Chen, J.; Nikolovska-Coleska, Z.; Wang, S. Importance of ligand reorganization free energy in protein-ligand binding affinity prediction. J. Am. Chem. Soc., 2009, 131, 13709-13721.
 
[494]  Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; Hermans, J. Interaction models of water in relation to protein hydration. In: Intermolecular forces: Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry, B. 1981, Pullman, Editor, pp. 331-342, Reidel Publ. Company, Dordrecht, Holland.
 
[495]  Berendsen, H. J. C.; Grigera, J. A.; Straatsma, T. P. The missing term in effective pair potentials. J. Phys. Chem., 1987, 91, 6269-5271.
 
[496]  Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 1983, 79, 926-935.
 
[497]  Mahoney, M. W.; Jorgensen, W. L. A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. J. Chem. Phys., 2000. 112, 8910-8922.
 
[498]  Wang, J.; Wang, W.; Huo, S.; Lee, M.; Kollman, P. A. Solvation model based on weighted solvent accessible surface area. J. Phys. Chem. B, 2001, 105, 5055-5067.
 
[499]  Michel, J.; Verdonk, M. L.; Essex, J. W. Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? J. Med. Chem,. 2006, 49, 7427-7439
 
[500]  Roux, B. Implicit solvent models. In: Computational Biochemistry and Biophysics, Becker, 2001, O.M., Mackerell, A. D. Jr, Roux, B., Watanabe, M. Editors, pp. 133-151, Marcel Dekker Inc., New York
 
[501]  Roux, B.; Simonson, T. Implicit solvent models. Biophys. Chem., 1999, 78, 1-20.
 
[502]  Schwab, F.; van Gunsteren, W. F.; Zagrovic, B. Computational study of the mechanism and the relative free energies of binding of anticholesteremic inhibitors to squalene-hopene cyclase. Biochemistry, 2008, 47, 2945-2951.
 
[503]  Harvey, M. J.; Giupponi, G.; De Fabritiis, G. ACEMD: Accelerating biomolecular simulations in the microsecond time scale. J. Chem. Theor.Comput., 2009, 5, 1632-1639.
 
[504]  Stone, J. E.; Hardy, D. J.; Isralewitz, B.; Schulten, K. GPU algorithms for molecular modeling. Chapter 16in: Scientific Computing with Multicore & Accelerators, Dongarra J.; Bader J.A; Kurzak J., 2011,351-371, Chapman & Hall/CRC Press.
 
[505]  Boresch, S.; Bruckner, S. Avoiding the van der Waals endpoint problem using serial atomic insertion. J. Comput. Chem., 2011, 32, 2449-2458.
 
[506]  Gohlke, H.; Kiel, C.; Case, D. A. Insight into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. J. Mol. Biol,. 2003, 330, 891-913.
 
[507]  van Gunsteren, W. F.; Berendsen, H. J. C. Computer simulation of molecular dynamics: methodology, applications, and perspectives in chemistry. Angew. Chem. Int. Ed. Engl., 1990, 29, 992-1023.
 
[508]  Mark, A. E.; van Helden, S. P.; Smith, P. E.; Janssen, L. H. M.; van Gunsteren. W. F. Convergence properties of free energy calculations: α-cyclodextrin complexes as a case study. J. Am. Chem. Soc., 1994, 116, 6293-6302.
 
[509]  Mark, A. E. Free energy perturbation calculations. In Schleyer, P. V. R., Allinger, N. L.; Clark, T.; Gasteiger, J.; Kollman, P. A.; Schaefer, H. F. III; Schreiner, P.R. editors, Encyclopedia of computational chemistry, 1998, 1070-1083, Wiley and Sons, Chichester.
 
[510]  Dixit, S. B.; Chipot, C. Can absolute free energies of association be estimated from molecular mechanical simulations? The biotin-streptavidin system revisited. J. Phys. Chem. A, 2001, 105, 9795-9799.
 
[511]  van Gunsteren, W. F.; Daura, X.; Mark, A. E. Computation of free energy. Helv. Chim. Acta, 2002, 85, 3113-3129.
 
[512]  Chipot, C.; Pearlman, D. A. Free energy calculations. The long and winding gilded road. Mol. Sim., 2002, 28, 1-12.
 
[513]  Piana, S.; Klepeis, J. L.; Shaw, D. E. Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Current Opin. Struct. Biol., 2014, 24, 98-105.
 
[514]  Marelius, J.; Hansson, T.; Aqvist, J. Calculation of ligand binding free energies from molecular dynamics simulations. Int. J. Quantum. Chem., 1988, 69, 77-88.
 
[515]  Zhao, C.; Caplan, D. A.; Noskov, S. Y. Evaluations of the absolute and relative free energies for antidepressant binding to the aminoacid membrane transporter leut with free energy simulations. J. Chem. Theory Comput., 2010, 6, 1900-1914.
 
[516]  Hulten, J.; Bonham, N. M.; Nillroth, U.; Hansson, T.; Zuccarello, G.; Bouzide, A.; Aqvist, J.; Classon, B.; Danielson, U. H.; Karlen, A.; Kvarnstrom, I.; Samuelsson, B.; Hallberg, A. Cyclic HIV-1 protease inhibitors derived from mannitol: synthesis, inhibitory potencies, and computational predictions of binding affinities. J. Med. Chem., 1997, 40, 885-897.
 
[517]  Homeyer, N.; Gohlke, H. FEW: a workflow tool for free energy calculations of ligand binding. J. Comput. Chem., 2013, 34, 965-973.
 
[518]  Marelius, J.; Kolmodin, K.; Feierberg, I.; Aqvist, J. Q: an md program for free energy calculations and empirical valence bond simulations in biomolecular systems. J. Mol. Graphics Modell., 1998, 16, 213-225.
 
[519]  van Gunsteren, W. F.; Berendsen, H. J. C. Groningen molecular simulation (GROMOS) library manual, Biomos; Nijenborgh: Groningen, The Netherlands, 1987.
 
[520]  Aqvist, J.; Mowbray, S. L. Sugar recognition by a glucose/galactose receptor: evaluation of binding energetics from molecular dynamics simulations. J. Biol. Chem., 1995, 270, 9978-9981.
 
[521]  Ersmark, K.; Feierberg, I.; Bjelic, S.; Hulten, J.; Samuelsson, B.; Aqvist, J.; Hallberg, A. C2-symmetric inhibitors of plasmodium falciparum Plasmepsin II: synthesis and theoretical predictions. Bioorg. Med. Chem., 2003, 11, 3723-3733.
 
[522]  Ersmark, K.; Feierberg, I.; Bjelic, S.; Hamelink, E.; Hackett, F.; Blackman, M. J.; Hulten, J.; Samuelsson, B.; Aqvist, J.; Hallberg, A. Potent inhibitors of the plasmodium falciparum enzymes Plasmepsin I and II devoid of Cathepsin D inhibitory activity. J. Med. Chem., 2004, 47, 110-122.
 
[523]  Marelius, J.; Graffner-Nordberg, M.; Hansson, T.; Hallberg, A.; Aqvist, J. Computation of affinity and selectivity: binding of 2,4-diaminopteridine and 2,4-diaminoquinazoline inhibitors to dihydrofolate reductases. J. Comput. Aided Mol. Des., 1998, 12, 119-131.
 
[524]  Ersmark, K.; Samuelsson, B.; Hallberg, A. Plasmepsins as potential targets for new antimalarial therapy. Med. Res. Rev., 2006, 26, 626-666.
 
[525]  Ljungberg, K. B.; Marelius, J.; Musil, D.; Svensson, P.; Norden, B.; Aqvist, J. Computational Modelling of Inhibitor Binding to Human Thrombin. Eur. J. Pharm. Sci., 2001, 12, 441-446.
 
[526]  Valiente, P. A.; Gil, A.; Batista, P. R.; Caffarena, E. R.; Pons, T.; Pascutti, P. G. New parameterization approaches of the LIE method to improve free energy calculations of plmii-inhibitors complexes. J. Comput. Chem., 2010, 31, 2723-2734.
 
[527]  Miranda, W. E.; Noskov, S. Y.; Valiente, P.A. Improving the LIE method for binding free energy calculations of protein-ligand complexes. J. Chem. Inf. Model., 2015, 55, 1867-1877.
 
[528]  Rastelli, G.; Del Rio, A.; Degliesposti, G.; Sgobba M. Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. J. Comput. Chem., 2010, 31, 797-810.
 
[529]  Sitkoff, D.; Sharp, K. A.; Honig, B. Accurate calculation of hydration free-energies using macroscopic solvent models. J. Phys. Chem., 1994, 98, 1978-1988.
 
[530]  Janezic, D.; Brooks, B. R. Harmonic-analysis of large systems. 2. Comparison of different protein models. J. Comput. Chem., 1995, 16, 1543-1553.
 
[531]  Janezic, D.; Venable, R. M.; Brooks, B. R. Harmonic-analysis of large systems. 3. Comparison with molecular dynamics. J. Comput. Chem., 1995, 16, 1554-1566.
 
[532]  Chong, L. T.; Duan, Y.; Wang, L.; Massova, I.; Kollman, P. A. Molecular dynamics and free-energy calculations applied to affinity maturation in antibody 48G7. Proc. Natl. Acad. Sci. USA., 1999, 96, 14330-14335.
 
[533]  Massova, I.; Kollman, P. A. Computational alanine scanning to probe protein-protein interactions: a novel approach to evaluate binding free energies. J. Am. Chem. Soc., 1999, 121, 8133-8143.
 
[534]  Reyes, C. M.; Kollman, P. A. Investigating the binding specificity of U1A-RNA by computational mutagenesis. J. Mol. Biol., 2000, 295, 1-6.
 
[535]  Prime, version 3.0, Schrödinger, LLC, New York, 2011.
 
[536]  Jacobson, M. P.; Pincus, D. L.; Rapp, C, S.; Day, T. J. F.; Honig, B.; Shaw, D. E.; Friesner, R. A. A hierarchical approach to all-atom protein loop prediction. Proteins Struct. Funct. Bioinf., 2004, 55, 351-367.
 
[537]  Miller B. R. III; McGee, T. D; Swails, J. M.; Homeyer, N.; Gohlke, H.; Roitberg, A. E. MMPBSA.py: An efficient program for end-state free energy calculations. J. Chem. Theor. Comput., 2012, 8, 3314-3321.
 
[538]  Case, D. A.; Darden, T. A.; Cheatham, T. E. III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R.C.; Zhang, W.; Merz, K. M.; Roberts, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Swails, J.; Goetz, A. W.; Kolossváry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wolf, R. M.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang, J.; Hsieh, M.-J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Salomon-Ferrer, R.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman P.A. AMBER 12, 2012, University of California, San Francisco.
 
[539]  Liu, X.; Liu, J.; Zhu, T.; Zhang, L.; He, X.; Zhang, J. Z. H. PBSA_E: A PBSA-based free energy estimator for protein-ligand binding affinity. J. Chem. Inf. Model., 2016, 56, 854-861.
 
[540]  Polydoridis, S.; Leonidas, D. D.; Oikonomakos, N. G.; Archontis G. Recognition of ribonuclease A by 3’-5’-pyrophosphate-linked dinucleotide inhibitors: a molecular dynamics/continuum electrostatics analysis. Biophys. J., 2007, 92, 1659-1672.
 
[541]  Lyubartsev, A. P.; Forrisdahl, O. K.; Laaksonen, A. Solvation free energies of methane and alkali halide ion pairs: an expanded ensemble molecular dynamics simulation study. J. Chem. Phys., 1998, 108, 227-233.
 
[542]  Lyubartsev, A. P.; Laaksonen, A.; Vorontsovvelyaminov, P. N. Free energy calculations for Lennard-Jones systems and water using the expanded ensemble method. A Monte Carlo and molecular dynamics simulation study. Mol. Phys., 1994, 82, 455-471.
 
[543]  Aberg, K. M.; Lyubartsev, A. P.; Jacobsson, S. P.; Laaksonen, A. Determination of solvation free energies by adaptive expanded ensemble molecular dynamics. J. Chem. Phys., 2004, 120, 3770-3776.
 
[544]  Fajer, M.; Hamelberg, D.; McCammon, J. A. Replica-exchange accelerated molecular dynamics (REXAMD) applied to thermodynamic integration. J. Chem. Theory. Comput., 2008, 4, 1565-1569.
 
[545]  Straatsma, T. P.; Berendsen, H. J. C. Free energy of ionic hydration: Analysis of a thermodynamic integration technique to evaluate free energy differences by molecular dynamics simulations. J. Chem. Phys., 1988, 89, 5876-5886.
 
[546]  Chaitanya, A. K.; Koppisetty, M. F.; Lyubartsev, A. P.; Nyholm, P.-G. Binding energy calculations for hevein-carbohydrate interactions using expanded ensemble molecular dynamics simulations. J. Comput. Aided Mol. Des., 2015, 29, 13-21
 
[547]  Huang, D.; Caflisch, A. Small molecule binding to proteins: affinity and binding/unbinding dynamics from atomistic simulations. ChemMedChem, 2011, 6, 1578-1580.
 
[548]  Huang, D.; Caflisch, A. The free energy landscape of small molecule unbinding. PLoS Comput. Biol., 2011, 7, e1002002.
 
[549]  Grubmüller, H.; Heymann, B.; Tavan, P. Ligand binding: molecular mechanics calculation of the streptavidin-biotin rupture force. Science, 1996, 271, 997-999.
 
[550]  Isralewitz, B.; Gao, M.; Schulten, K. Steered molecular dynamics and mechanical functions of proteins. Curr. Opin. Struct. Biol., 2001, 11, 224-230.
 
[551]  Isralewitz, B.; Baudry, J.; Gullingsrud, J.; Kosztin, D.; Schulten, K. Steered molecular dynamics investigations of protein function. J. Mol. Graphics Modell., 2001, 19, 13-25.
 
[552]  Lu, H.; Isralewitz, B.; Krammer, A.; Vogel, V.; Schulten, K. Unfolding of titin immunoglobulin domains by steered molecular dynamics simulation. Biophys. J., 1998, 75, 662-671.
 
[553]  Giorgino, T.; Fabritiis, G. D. A High-throughput steered molecular dynamics study on the free energy profile of ion permeation through Gramicidin A. J. Chem. Theory Comput., 2011, 7, 1943-1950.
 
[554]  Gwan, J. F.; Baumgaertner, A. Cooperative transport in a potassium ion channel. J. Chem. Phys., 2007, 127, 045103.
 
[555]  Hénin, J.; Schulten, E. T. K.; Chipot, C. Diffusion of glycerol through escherichia coli aquaglyceroporin GlpF. Biophys. J., 2008, 94, 832-839.
 
[556]  Jensen, M. Ø.; Park, S.; Tajkhorshid, E.; Schulten, K. Energetics of glycerol conduction through aquaglyceroporin GlpF. Proc. Natl. Acad. Sci., 2002, 99, 6731-6736.
 
[557]  Xu, Y.; Shen, J.; Luo, X.; Silman, I.; Sussman, J. L.; Chen, K.; Jiang, H. How does huperzine A enter and leave the binding gorge of acetylcholinesterase? Steered molecular dynamics simulations. J. Am. Chem. Soc., 2003, 125, 11340-11349.
 
[558]  Shen, L.; Shen, J.; Luo, X.; Cheng, F.; Xu, Y.; Chen, K.; Arnold, E.; Ding, J.; Jiang, H. Steered molecular dynamics simulation on the binding of NNRTI to HIV-1RT. Biophys, J., 2003, 84, 3547-3563.
 
[559]  Binnig, G.; Quate, C. F.; Berger, C. H. Atomic force microscope. Phys. Rev. Lett., 1986, 56, 930-933.
 
[560]  Simmons, R.; Finer, J.; Chu, S.; Spudich, J. Quantitative measurements of force and displacement using an optical trap. Biophys. J., 1996, 70, 1813-1822.
 
[561]  Amblard, F.; Yurke, B.; Pargellis, A.; Leibler, S. Magnetic manipulation for studying local rheology and micromechanical properties of biological systems. Rev. Sci. Instrum., 1996, 67, 1-10.
 
[562]  Florin, E. L.; Moy, V. T.; Gaub, H. E. Adhesion forces between individual ligand-receptor pairs. Science, 1994, 264, 415-417.
 
[563]  Kumar, S.; Li, M. S. Biomolecules under mechanical force. Phys. Reports, 2010, 486, 1-74.
 
[564]  Li, M. S. Secondary structure, mechanical stability and location of transition state of proteins. Biophys. J., 2007, 93, 2644-2654.
 
[565]  Rief, M.; Gautel, M.; Oesterhelt, F.; Fernandez, J. M.; Gaub, H. E. Reversible unfolding of individual titin immunoglobulin domains by AFM. Science, 1997, 276, 1109-1112.
 
[566]  Li, M. S.; Mai, B. K. Steered molecular dynamics-a promising tool for drug design. Current Bioinformatics, 2012, 7, 342-351
 
[567]  Mai, B. K.; Viet, M. H.; Li, M. S. Top-leads for swine influenza A/H1N1 virus revealed by steered molecular dynamics approach. J. Chem. Inf. Model., 2010, 50, 2236-2247.
 
[568]  Petrek, M.; Otyepka, M.; Banas, P.; Kosinova, P.; Koca, J.; Damborsky, J. CAVER: a new tool to explore routes from protein clefts, pockets and cavities. BMC Bioinformatics, 2006, 7, 316.
 
[569]  Zhou, R. H.; Friesner, R. A.; Ghosh, A.; Rizzo, R. C.; Jorgensen, W. L.; Levy, R. M. New linear interaction method for binding affinity calculations using a continuum solvent model. J. Phys. Chem. B, 2001, 105, 10388-10397.
 
[570]  Vuong, Q. V.; Nguyen, T. T.; Li, M. S. A new method for navigating optimal direction for pulling ligand from binding pocket: application to ranking binding affinity by steered molecular dynamics. J. Chem. Inf. Model., 2015, 55, 2731-2738
 
[571]  Andér, M.; Luzhkov, V. B.; Aqvist, J. Ligand binding to the voltage-gated Kv1.5 potassium channel in the open state—docking and computer simulations of a homology model. Biophys. J., 2008, 94, 820-831.
 
[572]  Carlsson, J.; Boukharta, L.; Aqvist, J. Combining docking, molecular dynamics and the linear interaction energy method to predict binding modes and affinities for non-nucleoside inhibitors to HIV-1 reverse transcriptase. J. Med. Chem., 2008, 51, 2648-2656.
 
[573]  Nervall, M.; Hanspers, P.; Aqvist, J. Predicting binding modes from free energy calculations. J. Med. Chem., 2008, 51, 2657-2667.
 
[574]  Waszkowycz, B. Towards improving compound selection in structure-based virtual screening. Drug Discov. Today, 2008, 13, 219-226.
 
[575]  Foloppe, N.; Hubbard, R. Towards predictive ligand design with free-energy based computational methods. Curr. Med. Chem., 2006, 13, 3583-3608.
 
[576]  Ahmed, M.; Sadek, M. M.; Abouzid, K. A.; Wang, F. In silico design, extended molecular dynamic simulations and binding energy calculations for a new series of dually acting inhibitors against EGFR and HER2. J. Mol. Graph. Model., 2013, 44, 220-231.
 
[577]  Zhao, H.; Caflisch, A. Molecular dynamics in drug design. Eur. J. Med. Chem., 2015, 91, 4-14.
 
[578]  Spiliotopoulos, D.; Caflisch, A. Molecular dynamics simulations of bromodomains reveal binding-site flexibility and multiple binding modes of the natural ligand acetyl-lysine. Isr. J. Chem., 2014, 54, 1084-1092.
 
[579]  Huang, D.; Caflisch, A. Library screening by fragment-based docking. J. Mol. Recognit., 2010, 23, 183-193.
 
[580]  Pieffet, G. P. The application of molecular dynamics simulation techniques and free energy calculations to predict protein-protein and protein-ligand interactions, Ph. D. Dissertation, Groningen University, 2005
 
[581]  Villa, A.: Mark, A. E. Calculation of free energy of solvation for neutral analogs of amino acid side chains. J. Comp. Chem., 2002, 23, 548-553.
 
[582]  Shirts, M. R.; Pitera, J. W.; Swope, W. C.; Pande, V, S. Extremely precise free energy calculations of amino acid side chain analogs: comparison of common molecular mechanics force fields for proteins. J. Chem. Phys., 2003, 119, 5740-5761.