Computational Drug Discovery and Design

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using enhanced conformational sampling algo-
rithms. Biochim Biophys Acta
1858:1635–1651


  1. Arthur EJ, Brooks CL III (2016) Efficient
    Implementation of constant pH molecular
    dynamics on modern graphics processors. J
    Comput Chem 37:2171–2180

  2. Ge H, Wang Y, Li C et al (2013) Molecular
    dynamics-based virtual screening: accelerating
    the drug discovery process by high-
    performance computing. J Chem Inf Model
    53:2757–2764

  3. Iakovou G, Hayward S, Laycock SD (2015)
    Adaptive GPU-accelerated force calculation
    for interactive rigid molecular docking using
    haptics. J Mol Graph Model 61:1–12

  4. Kazachenko S, Giovinazzo M, Hall KW, Cann
    NM (2015) Algorithms for GPU-based molec-
    ular dynamics simulations of complex fluids:
    applications to water, mixtures, and liquid crys-
    tals. J Comput Chem 36:1787–1804

  5. Kutzner C, Pall S, Fechner M, Esztermann A,
    de Groot BL, Grubmu ̈ller H (2015) Best bang
    for your buck: GPU nodes for GROMACS
    biomolecular simulations. J Comput Chem
    36:1990–2008

  6. Salomon-Ferrer R, Case DA, Walker RC
    (2013) An overview of the Amber biomolecu-
    lar simulation package. WIREs Comput Mol
    Sci 3:198–210

  7. Trott O, Olson AJ (2010) AutoDock Vina:
    improving the speed and accuracy of docking
    with a new scoring function, efficient optimiza-
    tion, and multithreading. J Comput Chem
    31:455–461

  8. Morris GM, Huey R, Lindstrom W, Sanner
    MF, Belew RK, Goodsell DS, Olson AJ
    (2009) AutoDock4 and AutoDockTools4:
    automated docking with selective receptor flex-
    ibility. J Comput Chem 30:2785–2791

  9. Eswar N, Eramian D, Webb B, Shen M-Y, Sali
    A (2008) Protein structure modeling with
    MODELLER. In: Kobe B, Guss M, Huber T
    (eds) Structural proteomics. High-throughput
    methods. Methods in molecular biology, vol
    426. Humana, Totowa, NJ, pp 145–159

  10. Song Y, DiMaio F, Wang RY-R, Kim D,
    Miles C, Brunette T, Thompson J, Baker D
    (2013) High resolution comparative modeling
    with RosettaCM. Structure 21:1735–1742

  11. Kelley LA, Mezulis S, Yates CM, Wass MN,
    Sternberg MJ (2015) The Phyre2 web portal
    for protein modeling, prediction and analysis.
    Nat Protoc 10:845–858

  12. McGann M (2011) FRED pose prediction and
    virtual screening accuracy. J Chem Inf Model
    51:578–596
    52. Irwin JJ, Shoichet BK (2005) ZINC-a free
    database of commercially available compounds
    for virtual screening. J Chem Inf Model
    45:177–182
    53. Baell J, Walters MA (2014) Chemical con artists
    foil drug discovery. Nature 513:481–483
    54. Wang J, Wolf RM, Caldwell JW, Kollman PA,
    Case DA (2004) Development and testing of a
    general amber force field. J Comput Chem
    25:1157–1174
    55. Chong S-H, Ham S (2015) Structural versus
    energetic approaches for protein conforma-
    tional entropy. Chem Phys Lett 627:90–95
    56. Kassem S, Marawan A, El-Sheikh S, Barakat
    KH (2015) Entropy in bimolecular simula-
    tions: a comprehensive review of atomic
    fluctuations-based methods. J Mol Graph
    Model 62:105–117
    57. Procacci P (2016) Reformulating the entropic
    contribution in molecular docking scoring
    functions. J Comput Chem 37:1819–1827
    58. Genheden S, Ryde U (2015) The MM/PBSA
    and MM/GBSA methods to estimate ligand-
    binding affinities. Expert Opin Drug Discovery
    10:449–461
    59. Vosmeer CR, Pool R, van Stee MF, Peric-
    Hassler L, Vermeulen NPE, Geerke DP
    (2014) Towards automated binding affinity
    prediction using an iterative linear interaction
    energy approach. Int J Mol Sci 15:798–816
    60. Rosendahl Kjellgren E, Skytte Glue OE,
    Reinholdt P, Egeskov Meyer J, Kongsted J,
    Poongavanam V (2015) A comparative study
    of binding affinities for6,7-dimethoxy-4-pyrro-
    lidylquinazolines as phosphodiesterase 10 A
    inhibitors using the linear interaction energy
    method. J Mol Graph Model 61:44–52
    61. Stjernschantz E, Oostenbrink C (2010)
    Improved ligand-protein binding affinity pre-
    dictions using multiple binding modes. Bio-
    phys J 98:2682–2691
    62. Miller BR III, McGee TD, Swails JM,
    Homeyer N, Gohlke H, Roitberg AE (2012)
    MMPBSA.py: an efficient program for
    end-state free energy calculations. J Chem The-
    ory Comput 8:3314–3321
    63. Borhani DW, Shaw DE (2012) The future of
    molecular dynamics simulations in drug dis-
    covery. J Comput Aided Mol Des 26:15–26
    64. Decherchi S, Masetti M, Vyalov I, Rocchia W
    (2015) Implicit solvent methods for free
    energy estimation. Eur J Med Chem 91:27–42
    65. Le L (2012) Incorporating molecular dynamics
    simulations into rational drug design: a case
    study on influenza a neuraminidases. In:
    Pe ́rez-Sa ́nchez H (ed) Bioinformatics. InTech,
    Rijeka


Molecular Dynamics in Virtual Screening 177
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