Computational Drug Discovery and Design

(backadmin) #1

  1. Ewing TJ, Makino S, Skillman AG, Kuntz ID
    (2001) DOCK 4.0: search strategies for auto-
    mated molecular docking of flexible molecule
    databases. J Comput Aided Mol Des
    15:411–428

  2. Gorelik B, Goldblum A (2008) High quality
    binding modes in docking ligands to proteins.
    Proteins 71:1373–1386

  3. Morris GM, Goodsell DS, Huey R, Olson AJ
    (1996) Distributed automated docking of flex-
    ible ligands to proteins: parallel applications of
    AutoDock 2.4. J Comput Aided Mol Des
    10:293–304

  4. Jones G, Willett P, Glen RC, Leach AR, Taylor
    R (1997) Development and validation of a
    genetic algorithm for flexible docking. J Mol
    Biol 267:727–748

  5. Santos RN, Andricopulo AD (2013) Physics
    and its interfaces with medicinal chemistry
    and drug design. Braz J Phys 43:268–280

  6. Foloppe N, Hubbard R (2006) Towards pre-
    dictive ligand design with free-energy based
    computational methods? Curr Med Chem
    13:3583–3608

  7. Huang SY, Grinter SZ, Zou X (2010) Scoring
    functions and their evaluation methods for pro-
    tein–ligand docking: recent advances and
    future directions. Phys Chem Chem Phys
    12:12899–12908

  8. Murray C, Auton TR, Eldridge MD (1998)
    Empirical scoring functions. II. The testing of
    an empirical scoring function for the prediction
    of ligand-receptor binding affinities and the use
    of Bayesian regression to improve the quality of
    the model. J Comput Aided Mol Des
    12:503–519

  9. Huang SY, Zou X (2006) An iterative
    knowledge-based scoring function to predict
    protein–ligand interactions: I. Derivation of
    interaction potentials. J Comput Chem
    27:1866–1875

  10. Mysinger MM, Shoichet BK (2010) Rapid
    context-dependent ligand desolvation in
    molecular docking. J Chem Inf Model
    50:1561–1573

  11. Ruvinsky AM (2007) Role of binding entropy
    in the refinement of protein–ligand docking
    predictions: analysis based on the use of
    11 scoring functions. J Comput Chem
    28:1364–1372

  12. Lionta E, Spyrou G, Vassilatis DK, Cournia Z
    (2014) Structure-based virtual screening for
    drug discovery: principles, applications and
    recent advances. Curr Top Med Chem
    14:1923–1938

  13. Scior T, Bender A, Tresadern G, Medina-
    Franco JL, Martı ́nez-Mayorga K, Langer T,


Cuanalo-Contreras K, Agrafiotis DK (2012)
Recognizing pitfalls in virtual screening: a crit-
ical review. J Chem Inf Model 52:867–881


  1. Jain AN, Nicholls A (2008) Recommendations
    for evaluation of computational methods. J
    Comput Aided Mol Des 22:133–139

  2. Moura Barbosa AJ, Del Rio A (2012) Freely
    accessible databases of commercial compounds
    for high- throughput virtual screenings. Curr
    Top Med Chem 12:866–877

  3. Rose PW, Prlic ́A, Ali A et al (2017) The RCSB
    protein data bank: integrative view of protein,
    gene and 3D structural information. Nucleic
    Acids Res 45:D271–D281

  4. Valli M, dos Santos RN, Figueira LD et al
    (2013) Development of a natural products
    database from the biodiversity of Brazil. J Nat
    Prod 76:439–444

  5. Williams AJ (2008) Public chemical compound
    databases. Curr Opin Drug Discov Devel
    11:393–404

  6. Nicola G, Liu T, Gilson MK (2012) Public
    domain databases for medicinal chemistry. J
    Med Chem 55:6987–7002

  7. Williams A, Tkachenko V (2014) The Royal
    Society of Chemistry and the delivery of chem-
    istry data repositories for the community. J
    Comput Aided Mol Des 28:1023–1030

  8. Irwin JJ, Sterling T, Mysinger MM et al (2012)
    ZINC: a free tool to discover chemistry for
    biology. J Chem Inf Model 52:1757–1768

  9. 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

  10. Pirhadib S, Sunseria J, Koes DR (2016) Open
    source molecular modeling. J Mol Graph
    Model 69:127–143

  11. O’Boyle NM, Banck M, James CA et al (2011)
    Open babel: an open chemical toolbox. J Che-
    minform 3:33

  12. Pettersen EF, Goddard TD, Huang CC et al
    (2004) UCSF chimera: a visualization system
    for exploratory research and analysis. J Comput
    Chem 25:1605–1612

  13. Knight ZA, Gonzalez B, Feldman ME et al
    (2006) A pharmacological map of the PI3-K
    family defines a role for p110alpha in insulin
    signaling. Cell 125:733–747

  14. Wu P, Liu T, Hu Y (2009) PI3K inhibitors for
    cancer therapy: what has been achieved so far?
    Curr Med Chem 16:916–930

  15. Brana I, Siu LL (2012) Clinical development of
    phosphatidylinositol 3-kinase inhibitors for
    cancer treatment. BMC Med 10:161


Molecular Docking and Structure-Based Virtual Screening 49
Free download pdf