Skip to main content
SpringerLink
Log in
Book cover

Structural Bioinformatics: Applications in Preclinical Drug Discovery Process pp 109–175Cite as

In Silico Structure-Based Prediction of Receptor–Ligand Binding Affinity: Current Progress and Challenges

  • Chapter
  • First Online:

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 27))

Abstract

Structure-based in silico studies aiming to predict affinity of a set of ligands to their cognate receptor have been enjoying keen interest and attention of researchers in drug design around the globe since many decades, and made significant progress to increase its predictive power, even it has emerged as a complementary field to in vivo and in vitro studies in recent years. Structure-based drug discovery (SBDD) process whose success heavily relies on a careful selection of structure of receptor and ligands and its accuracy, completeness, and rigor of chosen model, imitation of the physiological condition in such in silico models, e.g., pH and solvation. Appropriateness of selected mechanism of binding concept and the realization in mathematical terms used in scoring methods have a strong influence on the accuracy too. However, constant identification of new targets using systems approach like genomics, proteomics, metabolomics , and network biology has led a paradigm shift from single or a couple of targets toward the appreciation of emerging role of a network of targets. The application of such strategies in study of complex diseases is gaining attention. Identification of binding sites of receptor and their characterization is important to be able to portray its interacting features. It involves the search of ligands which are able to possess the features, present them complementary to the binding site, so by docking the set of ligands to the binding pocket of the receptor, activity can be evaluated. In silico receptor–ligand binding affinity prediction from docking has witnessed rigid-receptor rigid-ligand to flexible-ligand rigid-receptor treatment, and nowadays docking studies, through sampling side chain rotations of the binding site residues, also account for the flexibility of binding pocket of the receptor in indirect way. Literature survey has shown progress in ranking ligands in order of affinity using reliable scoring functions to find potent scaffolds which can be further optimized to gain more affinity. Many methods include effect of solvation in binding processes, like considering conserved water positions in active sites (water maps), explicit water simulation in presence of ligand with receptor, free energy perturbation , and thermodynamic integration . Availability of many conformers of receptors and ligands in solution suggests the importance of entropy in estimation of binding affinity, but entropy component of binding free energy directly is not included in such studies. In spite of unprecedented advancement of computational modeling, faster simulation techniques, accurate solvation models and current best practices, the dependence of binding affinity on pH, estimation of entropy along with enthalpy in binding affinity, inclusion of conformational entropy of ligand and receptor, and modulation of flexibilities during complex formation are important challenges lying ahead. Therefore, an account of prowess and challenges in structure-based prediction of binding affinity addressed in present review will provide directions for its appropriate application, understanding its limitations and getting important feedbacks for its betterment.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Seifert MHJ, Wolf K, Vitt D (2003) Virtual high-throughput in silico screening. Biosilico 1:143–149. https://doi.org/10.1016/s1478-5382(03)02359-x

    Article  CAS  Google Scholar 

  2. Engin H, Gursoy A, Nussinov R, Keskin O (2014) Network-based strategies can help mono- and poly-pharmacology drug discovery: a systems biology view. Curr Pharm Des 20:1201–1207. https://doi.org/10.2174/13816128113199990066

    Article  CAS  PubMed  Google Scholar 

  3. Jones PG (1984) Crystal structure determination: a critical view. Chem Soc Rev 13:157–172. https://doi.org/10.1039/cs9841300157

    Article  CAS  Google Scholar 

  4. Billeter M, Wagner G, Wüthrich K (2008) Solution NMR structure determination of proteins revisited. J Biomol NMR 42:155–158. https://doi.org/10.1007/s10858-008-9277-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins Struct Funct Genet 47:409–443. https://doi.org/10.1002/prot.10115

    Article  CAS  PubMed  Google Scholar 

  6. Marrone TJ, Briggs JM, McCammon JA (1997) Structure-based drug design: computational advances. Annu Rev Pharmacol Toxicol 37:71–90. https://doi.org/10.1146/annurev.pharmtox.37.1.71

    Article  CAS  PubMed  Google Scholar 

  7. Perola E, Walters WP, Charifson PS (2004) A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins Struct Funct Genet 56:235–249. https://doi.org/10.1002/prot.20088

    Article  CAS  PubMed  Google Scholar 

  8. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949. https://doi.org/10.1038/nrd1549

    Article  CAS  PubMed  Google Scholar 

  9. Yuriev E, Ramsland PA (2013) Latest developments in molecular docking: 2010–2011 in review. J Mol Recognit 26:215–239. https://doi.org/10.1002/jmr.2266

    Article  CAS  PubMed  Google Scholar 

  10. Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24:149–164. https://doi.org/10.1002/jmr.1077

    Article  CAS  PubMed  Google Scholar 

  11. Yuriev E, Holien J, Ramsland PA (2015) Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J Mol Recognit 28:581–604. https://doi.org/10.1002/jmr.2471

    Article  CAS  PubMed  Google Scholar 

  12. Taylor RD, Jewsbury PJ, Essex JW (2002) A review of protein-small molecule docking methods. J Comput Aided Mol Des 16:151–166. https://doi.org/10.1023/a:1020155510718

    Article  CAS  PubMed  Google Scholar 

  13. Huang SY, Zou X (2010) Advances and challenges in protein-ligand docking. Int J Mol Sci 11:3016–3034. https://doi.org/10.3390/ijms11083016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen Y-C (2015) Beware of docking! Trends Pharmacol Sci 36:78–95. https://doi.org/10.1016/j.tips.2014.12.001

    Article  CAS  PubMed  Google Scholar 

  15. Acharya KR, Lloyd MD (2005) The advantages and limitations of protein crystal structures. Trends Pharmacol Sci 26:10–14. https://doi.org/10.1016/j.tips.2004.10.011

    Article  CAS  PubMed  Google Scholar 

  16. Petukh M, Stefl S, Alexov E (2013) The role of protonation states in ligand-receptor recognition and binding. Curr Pharm Des 19:4182–4190. https://doi.org/10.2174/1381612811319230004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Onufriev AV, Alexov E (2013) Protonation and pK changes in protein–ligand binding. Q Rev Biophys 46:181–209. https://doi.org/10.1017/s0033583513000024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Srivastava J, Barreiro G, Groscurth S et al (2008) Structural model and functional significance of pH-dependent talin-actin binding for focal adhesion remodeling. Proc Natl Acad Sci U S A 105:14436–14441. https://doi.org/10.1073/pnas.0805163105

    Article  CAS  Google Scholar 

  19. Ferrara P, Gohlke H, Price DJ et al (2004) Assessing scoring functions for protein-ligand interactions. J Med Chem 47:3032–3047. https://doi.org/10.1021/jm030489h

    Article  CAS  PubMed  Google Scholar 

  20. Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931. https://doi.org/10.1021/jm050362n

    Article  CAS  PubMed  Google Scholar 

  21. Rask-Andersen M, Almén MS, Schiöth HB (2011) Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 10:579–590. https://doi.org/10.1038/nrd3478

    Article  CAS  PubMed  Google Scholar 

  22. Finan C, Gaulton A, Kruger FA et al (2017) The druggable genome and support for target identification and validation in drug development. Sci Transl Med 9:1–16. https://doi.org/10.1126/scitranslmed.aag1166

    Article  CAS  Google Scholar 

  23. Civelek M, Lusis AJ (2014) Systems genetics approaches to understand complex traits. Nat Rev Genet 15:34–48. https://doi.org/10.1038/nrg3575

    Article  CAS  PubMed  Google Scholar 

  24. Csermely P, Korcsmáros T, Kiss HJM et al (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 138:333–408. https://doi.org/10.1016/j.pharmthera.2013.01.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhu P, Aliabadi HM, Uludağ H, Han J (2016) Identification of potential drug targets in cancer signaling pathways using stochastic logical models. Sci Rep 6:23078. https://doi.org/10.1038/srep23078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Torkamani A, Topol EJ, Schork NJ (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92:265–272. https://doi.org/10.1016/j.ygeno.2008.07.011

    Article  CAS  PubMed  Google Scholar 

  27. Druker BJ, Tamura S, Buchdunger E et al (1996) Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat Med 2:561–566. https://doi.org/10.1038/nm0596-561

    Article  CAS  PubMed  Google Scholar 

  28. Fabian MA, Biggs WH, Treiber DK et al (2005) A small molecule-kinase interaction map for clinical kinase inhibitors. Nat Biotechnol 23:329–336. https://doi.org/10.1038/nbt1068

    Article  CAS  PubMed  Google Scholar 

  29. Montgomery JA, Niwas S, Rose JD et al (1993) Structure-based design of inhibitors of purine nucleoside phosphorylase. 1. 9-(arylmethyl) derivatives of 9-deazaguanine. J Med Chem 36:55–69. https://doi.org/10.1021/jm00053a008

    Article  CAS  PubMed  Google Scholar 

  30. Ealick SE, Babu YS, Bugg CE et al (1993) Application of X-ray crystallographic methods in the design of purine nucleoside phosphorylase inhibitors. Ann N Y Acad Sci 685:237–247

    Article  CAS  PubMed  Google Scholar 

  31. Ealick SE, Babu YS, Bugg CE et al (1991) Application of crystallographic and modeling methods in the design of purine nucleoside phosphorylase inhibitors. Proc Natl Acad Sci U S A 88:11540–11544. https://doi.org/10.1073/pnas.89.20.9974c

    Article  Google Scholar 

  32. Erion MD, Niwas S, Rose JD et al (1993) Structure-based design of inhibitors of purine nucleoside phosphorylase. 3. 9-arylmethyl derivatives of 9-deazaguanine substituted on the arylmethyl group. J Med Chem 36:3771–3783. https://doi.org/10.1021/jm00076a004

    Article  CAS  PubMed  Google Scholar 

  33. Freire E (2008) Do enthalpy and entropy distinguish first in class from best in class? Drug Discov Today 13:869–874. https://doi.org/10.1016/j.drudis.2008.07.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ho M-C, Shi W, Rinaldo-Matthis A et al (2010) Four generations of transition-state analogues for human purine nucleoside phosphorylase. Proc Natl Acad Sci 107:4805–4812. https://doi.org/10.1073/pnas.0913439107

    Article  PubMed  PubMed Central  Google Scholar 

  35. Edwards AA, Mason JM, Clinch K et al (2009) Altered enthalpy-entropy compensation in picomolar transition state analogues of human purine nucleoside phosphorylase. Biochemistry 48:5226–5238. https://doi.org/10.1021/bi9005896

    Article  CAS  PubMed  Google Scholar 

  36. Ohtaka H, Freire E (2005) Adaptive inhibitors of the HIV-1 protease. Prog Biophys Mol Biol 88:193–208. https://doi.org/10.1016/j.pbiomolbio.2004.07.005

    Article  CAS  PubMed  Google Scholar 

  37. Muzammil S, Armstrong AA, Kang LW et al (2007) Unique thermodynamic response of tipranavir to human immunodeficiency virus type 1 protease drug resistance mutations. J Virol 81:5144–5154. https://doi.org/10.1128/jvi.02706-06

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Carbonell T, Freire E (2005) Binding thermodynamics of statins to HMG-CoA reductase. Biochemistry 44:11741–11748. https://doi.org/10.1021/bi050905v

    Article  CAS  PubMed  Google Scholar 

  39. Du X, Li Y, Xia Y-L et al (2016) Insights into protein-ligand interactions: mechanisms, models, and methods. Int J Mol Sci 17:144. https://doi.org/10.3390/ijms17020144

    Article  CAS  PubMed Central  Google Scholar 

  40. Amaral M., Kokh DB, Bomke J, Wegener A, Buchstaller HP, Eggenweiler HM, Matias P, Sirrenberg C, Wade RC, Frech M (2017) Protein conformational flexibility modulates kinetics and thermodynamics of drug binding. Nat Commun. https://doi.org/10.1038/s41467-017-02258-w

  41. De Sanctis V, Kattamis C, Canatan D et al (2017) β-thalassemia distribution in the old world: an ancient disease seen from a historical standpoint. Mediterr J Hematol Infect Dis 9:1–14. https://doi.org/10.4084/mjhid.2017.018

    Article  Google Scholar 

  42. Goss C, Giardina P, Degtyaryova D et al (2014) Red blood cell transfusions for thalassemia: results of a survey assessing current practice and proposal of evidence-based guidelines. Transfusion 54:1773–1781. https://doi.org/10.1111/trf.12571

    Article  PubMed  Google Scholar 

  43. Michlitsch J, Walters M (2008) Recent advances in bone marrow transplantation in hemoglobinopathies. Curr Mol Med 8:675–689. https://doi.org/10.2174/156652408786241393

    Article  CAS  PubMed  Google Scholar 

  44. Human Hemoglobin Structures (PDB) (2018) http://www.rcsb.org/pdb/results/results.do?tabtoshow=Current&qrid=205B68A9. Accessed 15 Feb 2018

  45. Brown EN, Ramaswamy S (2007) Quality of protein crystal structures. Acta Crystallogr D Biol Crystallogr 63:941–950. https://doi.org/10.1107/s0907444907033847

    Article  CAS  PubMed  Google Scholar 

  46. Wlodawer A, Minor W, Dauter Z, Jaskolski M (2008) Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures. FEBS J 275:1–21. https://doi.org/10.1111/j.1742-4658.2007.06178.x

    Article  CAS  PubMed  Google Scholar 

  47. Muller P (2009) Practical suggestions for better crystal structures. Crystallogr Rev 15:57–83. https://doi.org/10.1080/08893110802547240

    Article  CAS  Google Scholar 

  48. Deller MC, Rupp B (2015) Models of protein-ligand crystal structures: trust, but verify. J Comput Aided Mol Des 29:817–836. https://doi.org/10.1007/s10822-015-9833-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Carugo O (2018) How large B-factors can be in protein crystal structures. BMC Bioinform 19:1–9. https://doi.org/10.1186/s12859-018-2083-8

    Article  CAS  Google Scholar 

  50. Li Z, Lazaridis T (2005) The effect of water displacement on binding thermodynamics: Concanavalin A. J Phys Chem B 109:662–670. https://doi.org/10.1021/jp0477912

    Article  CAS  PubMed  Google Scholar 

  51. Young T, Abel R, Kim B et al (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. Proc Natl Acad Sci 104:808–813. https://doi.org/10.1073/pnas.0610202104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Snyder PW, Mecinovic J, Moustakas DT et al (2011) Mechanism of the hydrophobic effect in the biomolecular recognition of arylsulfonamides by carbonic anhydrase. Proc Natl Acad Sci 108:17889–17894. https://doi.org/10.1073/pnas.1114107108

    Article  PubMed  PubMed Central  Google Scholar 

  53. Sánchez R, Sali A (1997) Advances in comparative protein-structure modelling. Curr Opin Struct Biol 7:206–214

    Article  PubMed  Google Scholar 

  54. Eswar N, John B, Mirkovic N et al (2003) Tools for comparative protein structure modeling and analysis. Nucl Acids Res 31:3375–3380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291. https://doi.org/10.1107/s0021889892009944

    Article  CAS  Google Scholar 

  56. Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8:52–6, 29

    Article  CAS  PubMed  Google Scholar 

  57. McKinney JD, Zu Bentrup K Höner, Muñoz-Elias EJ et al (2000) Persistence of Mycobacterium tuberculosis in macrophages and mice requires the glyoxylate shunt enzyme isocitrate lyase. Nature 406:735–738. https://doi.org/10.1038/35021074

    Article  CAS  PubMed  Google Scholar 

  58. Tanjore Soundarajan Balganesh; Santanu Datta; Indira Ghosh (2004) WO2004087943A1

    Google Scholar 

  59. Nelson K, Wang FS, Boyd EF, Selander RK (1997) Size and sequence polymorphism in the isocitrate dehydrogenase kinase/phosphatase gene (aceK) and flanking regions in Salmonella enterica and Escherichia coli. Genetics 147:1509–1520

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Garnak M, Reeves H (1979) Phosphorylation of isocitrate dehydrogenase of Escherichia coli. Science 203(80):1111–1112. https://doi.org/10.1126/science.34215

    Article  CAS  PubMed  Google Scholar 

  61. Vinekar R, Verma C, Ghosh I (2012) Functional relevance of dynamic properties of dimeric NADP-dependent isocitrate dehydrogenases. BMC Bioinform 13:S2. https://doi.org/10.1186/1471-2105-13-s17-s2

    Article  CAS  Google Scholar 

  62. Vinekar R, Ghosh I (2009) Determination of phosphorylation sites for NADP-specific isocitrate dehydrogenase from Mycobacterium tuberculosis. J Biomol Struct Dyn 26:741–754. https://doi.org/10.1080/07391102.2009.10507286

    Article  CAS  PubMed  Google Scholar 

  63. Av-Gay Y, Everett M (2000) The eukaryotic-like Ser/Thr protein kinases of Mycobacterium tuberculosis. Trends Microbiol 8:238–244

    Article  CAS  PubMed  Google Scholar 

  64. Hurley JH, Thorsness PE, Ramalingam V et al (1989) Structure of a bacterial enzyme regulated by phosphorylation, isocitrate dehydrogenase. Proc Natl Acad Sci U S A 86:8635–8639. https://doi.org/10.1073/pnas.86.22.8635

    Article  CAS  Google Scholar 

  65. Ceccarelli C, Grodsky NB, Ariyaratne N et al (2002) Crystal structure of porcine mitochondrial NADP+-dependent isocitrate dehydrogenase complexed with Mn2+ and isocitrate: Insights into the enzyme mechanism. J Biol Chem 277:43454–43462. https://doi.org/10.1074/jbc.m207306200

    Article  CAS  PubMed  Google Scholar 

  66. Quartararo CE, Hazra S, Hadi T, Blanchard JS (2013) Structural, kinetic and chemical mechanism of isocitrate dehydrogenase-1 from Mycobacterium tuberculosis. Biochemistry 52:1765–1775. https://doi.org/10.1021/bi400037w

    Article  CAS  PubMed  Google Scholar 

  67. Hardin C, Pogorelov TV, Luthey-Schulten Z (2002) Ab initio protein structure prediction. Curr Opin Struct Biol 12:176–181

    Article  CAS  PubMed  Google Scholar 

  68. Bonneau R, Baker D (2001) Ab initio protein structure prediction. Annu Rev Biophys Biomol Struct 30:173–189

    Google Scholar 

  69. Moult J, Fidelis K, Kryshtafovych A et al (2017) Critical assessment of methods of protein structure prediction (CASP)—round XII. Proteins Struct Funct Bioinform 82:1–6

    Google Scholar 

  70. Singh A, Kaushik R, Mishra A et al (2016) ProTSAV: a protein tertiary structure analysis and validation server. Biochim Biophys Acta Proteins Proteomics 1864:11–19. https://doi.org/10.1016/j.bbapap.2015.10.004

    Article  CAS  Google Scholar 

  71. Novoa EM, de Pouplana LR, Barril X, Orozco M (2010) Ensemble docking from homology models. J Chem Theory Comput 6:2547–2557

    Article  CAS  PubMed  Google Scholar 

  72. Kadam RU, Juraszek J, Brandenburg B et al (2017) Potent peptidic fusion inhibitors of influenza virus. Science 358(80):496–502. https://doi.org/10.1126/science.aan051

  73. Chang YS, Graves B, Guerlavais V et al (2013) Stapled α-helical peptide drug development: a potent dual inhibitor of MDM2 and MDMX for p53-dependent cancer therapy. Proc Natl Acad Sci 110:E3445–E3454. https://doi.org/10.1073/pnas.1303002110

    Article  PubMed  PubMed Central  Google Scholar 

  74. Shadfan M, Lopez-Pajares V, Yuan Z-M (2012) MDM2 and MDMX: alone and together in regulation of p53. Transl Cancer Res 1:88–89. https://doi.org/10.3978/j.issn.2218-676x.2012.04.02

    Article  PubMed  Google Scholar 

  75. Tiwari G, Verma CS (2017) Toward understanding the molecular recognition of albumin by p53-activating stapled peptide ATSP-7041. J Phys Chem B 121:657–670. https://doi.org/10.1021/acs.jpcb.6b09900

    Article  CAS  PubMed  Google Scholar 

  76. Schindler CEM, De Vries SJ, Zacharias M (2015) Fully blind peptide-protein docking with pepATTRACT. Structure 23:1507–1515. https://doi.org/10.1016/j.str.2015.05.021

    Article  CAS  PubMed  Google Scholar 

  77. Leder L, Berger C, Bomhauser S et al (1995) Spectroscopic, calorimetric, and kinetic demonstration of conformational adaptation in peptide-antibody recognition. Biochemistry 34:16509–16518. https://doi.org/10.1021/bi00050a035

    Article  CAS  PubMed  Google Scholar 

  78. Ferrari AM, Wei BQ, Costantino L, Shoichet BK (2004) Soft docking and multiple receptor conformations in virtual screening. J Med Chem 47:5076–5084. https://doi.org/10.1021/jm049756p

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Totrov M, Ferna J, Abagyan R (2002) Soft protein–protein docking in internal coordinates. Protein Sci 11:280–291. https://doi.org/10.1110/ps.19202.ical

    Article  PubMed  PubMed Central  Google Scholar 

  80. Li CH, Ma XH, Chen WZ, Wang CX (2003) A soft docking algorithm for predicting the structure of antibody-antigen complexes. Proteins Struct Funct Genet 52:47–50. https://doi.org/10.1002/prot.10382

    Article  CAS  PubMed  Google Scholar 

  81. Alonso H, Bliznyuk AA, Gready JE (2006) Combining docking and molecular dynamic simulations in drug design. Med Res Rev 26:531–568. https://doi.org/10.1002/med.20067

    Article  CAS  PubMed  Google Scholar 

  82. Leach AR (1994) Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol 235:345–356. https://doi.org/10.1016/s0022-2836(05)80038-5

    Article  CAS  PubMed  Google Scholar 

  83. Schnecke V, Kuhn LA (2000) Virtual screening with solvation and ligand-induced complementarity. Perspect Drug Discov Des 20:171–190. https://doi.org/10.1023/a:1008737207775

    Article  CAS  Google Scholar 

  84. Källblad P, Dean PM (2003) Efficient conformational sampling of local side-chain flexibility. J Mol Biol 326:1651–1665. https://doi.org/10.1016/s0022-2836(03)00083-4

    Article  PubMed  Google Scholar 

  85. Frimurer TM, Peters GH, Iversen LF et al (2003) Ligand-induced conformational changes: Improved predictions of ligand binding conformations and affinities. Biophys J 84:2273–2281. https://doi.org/10.1016/s0006-3495(03)75033-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Gaudreault F, Chartier M, Najmanovich R (2012) Side-chain rotamer changes upon ligand binding: common, crucial, correlate with entropy and rearrange hydrogen bonding. Bioinformatics 28:423–430. https://doi.org/10.1093/bioinformatics/bts395

    Article  CAS  Google Scholar 

  87. Apostolakis J, Plückthun A, Caflisch A (1998) Docking small ligands in flexible binding sites. J Comput Chem 19:21–37. https://doi.org/10.1002/(sici)1096-987x(19980115)19:1%3c21::aid-jcc2%3e3.0.co;2-0

  88. Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385:381–392. https://doi.org/10.1016/j.jmb.2008.11.010

    Article  CAS  PubMed  Google Scholar 

  89. Meiler J, Baker D (2006) ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins Struct Funct Bioinform 65:538–548. https://doi.org/10.1002/prot.21086

    Article  CAS  Google Scholar 

  90. Perryman AL, Lin JH, McCammon JA (2006) Optimization and computational evaluation of a series of potential active site inhibitors of the V82F/I84V drug-resistant mutant of HIV-1 protease: an application of the relaxed complex method of structure-based drug design. Chem Biol Drug Des 67:336–345. https://doi.org/10.1111/j.1747-0285.2006.00382.x

    Article  CAS  PubMed  Google Scholar 

  91. Shandilya A, Chacko S, Jayaram B, Ghosh I (2013) A plausible mechanism for the antimalarial activity of artemisinin: a computational approach. Sci Rep 3:2513

    Article  PubMed  PubMed Central  Google Scholar 

  92. Li J, Zhou B (2010) Biological actions of artemisinin: insights from medicinal chemistry studies. Molecules 15:1378–1397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Eckstein-Ludwig U, Webb RJ, Van Goethem IDA et al (2003) Artemisinins target the SERCA of Plasmodium falciparum. Nature 424:957

    Article  CAS  PubMed  Google Scholar 

  94. Pawan K, Shandilya A, Jayaram B, Ghosh I (2016) Integrative method for finding antimalarials using in silico approach. In: Kholmurodov KT (ed) Computer design for new drugs and materials. Nova Science Publishers, New York, NY, pp 13–38

    Google Scholar 

  95. Waelbroeck M (1982) The pH dependence of insulin binding. A quantitative study. J Biol Chem 257:8284–8291

    CAS  PubMed  Google Scholar 

  96. Ellis CR, Shen J (2015) pH-dependent population shift regulates BACE1 activity and inhibition. J Am Chem Soc 137:9543–9546. https://doi.org/10.1021/jacs.5b05891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Mongan J, Case DA, McCammon JA (2004) Constant pH molecular dynamics in generalized Born implicit solvent. J Comput Chem 25:2038–2048. https://doi.org/10.1002/jcc.20139

    Article  CAS  PubMed  Google Scholar 

  98. Kim MO, Blachly PG, McCammon JA (2015) Conformational dynamics and binding free energies of inhibitors of BACE-1: from the perspective of protonation equilibria. PLoS Comput Biol 11:1–28. https://doi.org/10.1371/journal.pcbi.1004341

    Article  CAS  Google Scholar 

  99. Hodge CN, Aldrich PE, Bacheler LT et al (1996) Improved cyclic urea inhibitors of the HIV-1 protease: synthesis, potency, resistance profile, human pharmacokinetics and X-ray crystal structure of DMP 450. Chem Biol 3:301–314. https://doi.org/10.1016/s1074-5521(96)90110-6

    Article  CAS  PubMed  Google Scholar 

  100. Lam PY, Jadhav PK, Eyermann CJ et al (1994) Rational design of potent, bioavailable, nonpeptide cyclic ureas as HIV protease inhibitors. Science 263(80):380–384. https://doi.org/10.1126/science.8278812

    Article  CAS  PubMed  Google Scholar 

  101. Kumalo HM, Bhakat S, Soliman MES (2015) Theory and applications of covalent docking in drug discovery: merits and pitfalls. Molecules 20:1984–2000. https://doi.org/10.3390/molecules20021984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Lecomte M, Laneuville O, Ji C et al (1994) Acetylation of human prostaglandin endoperoxide synthase-2 (cyclooxygenase-2) by aspirin. J Biol Chem 269:13207–13215

    CAS  PubMed  Google Scholar 

  103. Hadváry P, Lengsfeld H, Wolfer H (1988) Inhibition of pancreatic lipase in vitro by the covalent inhibitor tetrahydrolipstatin. Biochem J 256:357–361. https://doi.org/10.1042/bj2560357

    Article  PubMed  PubMed Central  Google Scholar 

  104. Scarpino A, Ferenczy GG, Keserű GM (2018) Comparative evaluation of covalent docking tools. J Chem Inf Model acs.jcim.8b00228. https://doi.org/10.1021/acs.jcim.8b00228

    Article  CAS  PubMed  Google Scholar 

  105. Zhu K, Borrelli KW, Greenwood JR et al (2014) Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J Chem Inf Model 54:1932–1940. https://doi.org/10.1021/ci500118s

    Article  CAS  PubMed  Google Scholar 

  106. Bianco G, Forli S, Goodsell DS, Olson AJ (2016) Covalent docking using autodock: two-point attractor and flexible side chain methods. Protein Sci 25:295–301. https://doi.org/10.1002/pro.2733

    Article  CAS  PubMed  Google Scholar 

  107. Corbeil CR, Englebienne P, Moitessier N (2007) Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J Chem Inf Model 47:435–449. https://doi.org/10.1021/ci6002637

    Article  CAS  PubMed  Google Scholar 

  108. MOE: Molecular Operating Environment (2018) http://www.chemcomp.com/MOE-Molecular_Operating_Environment.htm. Accessed 7 Feb 2018

  109. Katritch V, Byrd CM, Tseitin V et al (2007) Discovery of small molecule inhibitors of ubiquitin-like poxvirus proteinase I7L using homology modeling and covalent docking approaches. J Comput Aided Mol Des 21:549–558. https://doi.org/10.1007/s10822-007-9138-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein–ligand docking using GOLD. Proteins Struct Funct Bioinform 623:609–623. https://doi.org/10.1002/prot.10465

    Article  CAS  Google Scholar 

  111. Domínguez JL, Christopeit T, Villaverde MC et al (2010) Effect of the protonation state of the titratable residues on the inhibitor affinity to BACE-1. Biochemistry 49:7255–7263. https://doi.org/10.1021/bi100637n

    Article  CAS  PubMed  Google Scholar 

  112. Perryman AL, Lin J (2004) HIV-1 protease molecular dynamics of a wild-type and of the V82F / I84V mutant: possible contributions to drug resistance and a potential new target site for drugs. Protein Sci 13:1108–1123. https://doi.org/10.1110/ps.03468904.importance

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Trylska J, Tozzini V, Chang CEA, McCammon JA (2007) HIV-1 protease substrate binding and product release pathways explored with coarse-grained molecular dynamics. Biophys J 92:4179–4187. https://doi.org/10.1529/biophysj.106.100560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Liu Z-P, Wu L-Y, Wang Y et al (2008) Bridging protein local structures and protein functions. Amino Acids 35:627–650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Ratnaparkhi GS, Varadarajan R (2000) Thermodynamic and structural studies of cavity formation in proteins suggest that loss of packing interactions rather than the hydrophobic effect dominates the observed energetics. Biochemistry 39:12365–12374

    Article  CAS  PubMed  Google Scholar 

  116. DesJarlais RL, Sheridan RP, Seibel GL et al (1988) Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structure. J Med Chem 31:722–729

    Article  CAS  PubMed  Google Scholar 

  117. Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797. https://doi.org/10.1016/j.chembiol.2003.09.002

    Article  CAS  PubMed  Google Scholar 

  118. Lee B, Richards FM (1971) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55:379–400

    Article  CAS  PubMed  Google Scholar 

  119. Connolly ML (1983) Analytical molecular surface calculation. J Appl Crystallogr 16:548–558

    Article  CAS  Google Scholar 

  120. Connolly ML (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science 221:709–713

    Article  CAS  PubMed  Google Scholar 

  121. Liang J, Woodward C, Edelsbrunner H (1998) Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci 7:1884–1897

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Edelsbrunner H (1995) Smooth surfaces for multi-scale shape representation. In: International conference on foundations of software technology and theoretical computer science, pp 391–412

    Google Scholar 

  123. Mücke EP (1998) A robust implementation for three-dimensional Delaunay triangulations. Int J Comput Geom Appl 8:255–276

    Article  Google Scholar 

  124. Bryant R, Edelsbrunner H, Koehl P, Levitt M (2004) The area derivative of a space-filling diagram. Discrete Comput Geom 32:293–308

    Article  Google Scholar 

  125. Dundas J, Ouyang Z, Tseng J et al (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:W116–W118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Benkaidali L, André F, Maouche B et al (2013) Computing cavities, channels, pores and pockets in proteins from non-spherical ligands models. Bioinformatics 30:792–800

    Article  PubMed  CAS  Google Scholar 

  127. Huang B, Schroeder M (2006) LIGSITE csc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct Biol 6:19

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Oliveira SHP, Ferraz FAN, Honorato RV et al (2014) KVFinder: steered identification of protein cavities as a PyMOL plugin. BMC Bioinform 15:197

    Article  CAS  Google Scholar 

  129. Brady GP, Stouten PFW (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14:383–401

    Article  CAS  PubMed  Google Scholar 

  130. Czirják G (2015) PrinCCes: continuity-based geometric decomposition and systematic visualization of the void repertoire of proteins. J Mol Graph Model 62:118–127

    Article  PubMed  CAS  Google Scholar 

  131. Yu J, Zhou Y, Tanaka I, Yao M (2009) Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. Bioinformatics 26:46–52

    Article  PubMed  CAS  Google Scholar 

  132. Sheffler W, Baker D (2009) RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation. Protein Sci 18:229–239

    CAS  PubMed  Google Scholar 

  133. Laskowski RA (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13:323–330

    Article  CAS  PubMed  Google Scholar 

  134. Kleywegt GJ, Jones TA (1994) Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr Sect D: Biol Crystallogr 50:178–185

    Article  CAS  Google Scholar 

  135. Jacoby E, Fauchère J-L, Raimbaud E et al (1999) A three binding site hypothesis for the interaction of ligands with monoamine g protein-coupled receptors: implications for combinatorial ligand design. Mol Inform 18:561–572

    CAS  Google Scholar 

  136. Deganutti G, Moro S (2017) Estimation of kinetic and thermodynamic ligand-binding parameters using computational strategies. Future Med Chem 9:507–523

    Article  CAS  PubMed  Google Scholar 

  137. Chiu SH, Xie L (2016) Toward high-throughput predictive modeling of protein binding/unbinding kinetics. J Chem Inf Model 56:1164–1174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Singh A (2014) Selectivity and specificity profiling of binding sites of SER/THR kinases: case study of homo sapiens and Plasmodium falciparum. Jawaharlal Nehru University

    Google Scholar 

  139. Hanks SK, Hunter T (1995) Protein kinases 6. The eukaryotic protein kinase superfamily: kinase (catalytic) domain structure and classification. FASEB J 9:576–596. https://doi.org/10.1096/fasebj.9.8.776834

  140. Freire E (2015) The binding thermodynamics of drug candidates. In: thermodynamics and kinetics of drug binding. Wiley Online Library, pp 1–13

    Google Scholar 

  141. Cross S, Baroni M, Carosati E et al (2010) FLAP: GRID molecular interaction fields in virtual screening. Validation using the DUD data set. J Chem Inf Model 50:1442–1450

    Article  CAS  PubMed  Google Scholar 

  142. Kaalia R, Kumar A, Srinivasan A, Ghosh I (2015) An ab initio method for designing multi-target specific pharmacophores using complementary interaction field of aspartic proteases. Mol Inform 34:380–393

    Article  CAS  PubMed  Google Scholar 

  143. Nakashima R, Sakurai K, Yamasaki S et al (2013) Structural basis for the inhibition of bacterial multidrug exporters. Nature 500:102

    Article  CAS  PubMed  Google Scholar 

  144. Koshland DE (1958) Application of a theory of enzyme specificity to protein synthesis. Proc Natl Acad Sci 44:98–104. https://doi.org/10.1073/pnas.44.2.98

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Debler EW, Müller R, Hilvert D, Wilson IA (2008) Conformational isomerism can limit antibody catalysis. J Biol Chem 283:16554–16560. https://doi.org/10.1074/jbc.m710256200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Doshi U, McGowan LC, Ladani ST, Hamelberg D (2012) Resolving the complex role of enzyme conformational dynamics in catalytic function. Proc Natl Acad Sci 109:5699–5704. https://doi.org/10.1073/pnas.1117060109

    Article  PubMed  PubMed Central  Google Scholar 

  147. Teague SJ (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2:527–541. https://doi.org/10.1038/nrd1129

    Article  CAS  PubMed  Google Scholar 

  148. Hawkins PCD (2017) Conformation generation: the state of the art. J Chem Inf Model 57:1747–1756. https://doi.org/10.1021/acs.jcim.7b00221

    Article  CAS  PubMed  Google Scholar 

  149. Grinter SZ, Zou X (2014) Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules 19:10150–10176. https://doi.org/10.3390/molecules190710150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Mobley DL, Dill KA (2009) Binding of small-molecule ligands to proteins: “what you see” is not always “what you get.” Structure 17:489–498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. London N, Movshovitz-Attias D, Schueler-Furman O (2010) The structural basis of peptide-protein binding strategies. Structure 18:188–199. https://doi.org/10.1016/j.str.2009.11.012

    Article  CAS  PubMed  Google Scholar 

  152. London N, Raveh B, Schueler-Furman O (2013) Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Curr Opin Struct Biol 23:894–902. https://doi.org/10.1016/j.sbi.2013.07.006

    Article  CAS  PubMed  Google Scholar 

  153. Bernetti M, Cavalli A, Mollica L (2017) Protein–ligand (un)binding kinetics as a new paradigm for drug discovery at the crossroad between experiments and modelling. Med Chem Commun 8:534–550. https://doi.org/10.1039/c6md00581k

    Article  CAS  Google Scholar 

  154. Pan AC, Borhani DW, Dror RO, Shaw DE (2013) Molecular determinants of drug-receptor binding kinetics. Drug Discov Today 18:667–673. https://doi.org/10.1016/j.drudis.2013.02.007

    Article  CAS  PubMed  Google Scholar 

  155. Case DA (1988) Dynamical simulation of rate constants in protein-ligand interactions. Prog Biophys Mol Biol 52:39–70. https://doi.org/10.1016/0079-6107(88)90007-7

    Article  CAS  PubMed  Google Scholar 

  156. Liu J, Wang R (2015) Classification of current scoring functions. J Chem Inf Model 55:475–482. https://doi.org/10.1021/ci500731a

    Article  CAS  PubMed  Google Scholar 

  157. Cornell WD, Cieplak P, Bayly CI et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  158. MacKerell AD Jr, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616

    Article  CAS  PubMed  Google Scholar 

  159. Allen WJ, Balius TE, Mukherjee S et al (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36:1132–1156. https://doi.org/10.1002/jcc.23905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Simonson T, Archontis G, Karplus M (2002) Free energy simulations come of age: protein-ligand recognition. Acc Chem Res 35:430–437

    Article  CAS  PubMed  Google Scholar 

  161. Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking. Proteins Struct Funct Bioinform 37:228–241

    Article  CAS  Google Scholar 

  162. Böhm H-J (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 8:243–256

    Article  PubMed  Google Scholar 

  163. Eldridge MD, Murray CW, Auton TR et al (1997) 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 11:425–445

    Article  CAS  PubMed  Google Scholar 

  164. Böhm H-J (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6:593–606

    Article  PubMed  Google Scholar 

  165. Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. https://doi.org/10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  166. Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11–26

    Article  CAS  PubMed  Google Scholar 

  167. Sippl MJ (1995) Knowledge-based potentials for proteins. Curr Opin Struct Biol 5:229–235

    Article  CAS  PubMed  Google Scholar 

  168. Lyne PD (2002) Structure-based virtual screening: an overview. Drug Discov Today 7:1047–1055. https://doi.org/10.1016/s1359-6446(02)02483-2

    Article  CAS  PubMed  Google Scholar 

  169. Muegge I (2000) A knowledge-based scoring function for protein-ligand interactions: probing the reference state. Perspect Drug Discov Des 20:99–114

    Article  CAS  Google Scholar 

  170. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337–356

    Article  CAS  PubMed  Google Scholar 

  171. Head RD, Smythe ML, Oprea TI et al (1996) VALIDATE: A new method for the receptor-based prediction of binding affinities of novel ligands. J Am Chem Soc 118:3959–3969

    Article  CAS  Google Scholar 

  172. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    Article  CAS  PubMed  Google Scholar 

  173. Morris GM, Goodsell DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662

    Article  CAS  Google Scholar 

  174. Spyrakis F, Amadasi A, Fornabaio M et al (2007) The consequences of scoring docked ligand conformations using free energy correlations. Eur J Med Chem 42:921–933. https://doi.org/10.1016/j.ejmech.2006.12.037

    Article  CAS  PubMed  Google Scholar 

  175. Oprea TI, Marshall GR (1998) Receptor-based prediction of binding affinities. Perspect Drug Discov Des 9:35–61

    Article  Google Scholar 

  176. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801. https://doi.org/10.1021/jm0608356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Empereur-Mot C, Guillemain H, Latouche A et al (2015) Predictiveness curves in virtual screening. J Cheminform 7:52. https://doi.org/10.1186/s13321-015-0100-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM (2013) Evaluation and optimization of virtual screening workflows with DEKOIS 2.0—a public library of challenging docking benchmark sets. J Chem Inf Model 53:1447–1462

    Article  CAS  PubMed  Google Scholar 

  179. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucl Acids Res 28:235–242. https://doi.org/10.1093/nar/28.1.235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Gore S, Sanz García E, Hendrickx PMS et al (2017) Validation of structures in the protein data bank. Structure 25:1916–1927. https://doi.org/10.1016/j.str.2017.10.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Van Zundert GCP, Rodrigues JPGLM, Trellet M, Schmitz C (2016) The HADDOCK2. 2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol 428:720–725. https://doi.org/10.1016/j.jmb.2015.09.014

    Article  CAS  Google Scholar 

  183. Diller DJ, Merz KM (2001) High throughput docking for library design and library prioritization. Proteins Struct Funct Genet 43:113–124. https://doi.org/10.1002/1097-0134(20010501)43:2%3c113:aid-prot1023%3e3.0.co;2-t

    Article  CAS  PubMed  Google Scholar 

  184. Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 21:289–307. https://doi.org/10.1016/s1093-3263(02)00164-x

    Article  CAS  PubMed  Google Scholar 

  185. Holton S, Merckx A, Burgess D et al (2003) Structures of P. falciparum PfPK5 test the CDK regulation paradigm and suggest mechanisms of small molecule inhibition. Structure 11:1329–1337. https://doi.org/10.1016/j.str.2003.09.020

    Article  CAS  PubMed  Google Scholar 

  186. Engels MFM, Gibbs AC, Jaeger EP et al (2006) A cluster-based strategy for assessing the overlap between large chemical libraries and its application to a recent acquisition. J Chem Inf Model 46:2651–2660. https://doi.org/10.1021/ci600219n

    Article  CAS  PubMed  Google Scholar 

  187. Krier M, Bret G, Rognan D (2006) Assessing the scaffold diversity of screening libraries. J Chem Inf Model 46:512–524. https://doi.org/10.1021/ci050352v

    Article  CAS  PubMed  Google Scholar 

  188. McGregor MJ, Muskal SM (1999) Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J Chem Inf Comput Sci 39:569–574. https://doi.org/10.1021/ci980159j

    Article  CAS  PubMed  Google Scholar 

  189. Reymond J-L, van Deursen R, Blum LC, Ruddigkeit L (2010) Chemical space as a source for new drugs. Medchemcomm 1:30. https://doi.org/10.1039/c0md00020e

    Article  CAS  Google Scholar 

  190. Williams AJ (2008) A perspective of publicly accessible/open-access chemistry databases. Drug Discov Today 13:495–501. https://doi.org/10.1016/j.drudis.2008.03.017

    Article  CAS  PubMed  Google Scholar 

  191. Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860. https://doi.org/10.2174/09298673113209990001

    Article  CAS  PubMed  Google Scholar 

  192. Hann MM, Oprea TI (2004) Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol 8:255–263. https://doi.org/10.1016/j.cbpa.2004.04.003

    Article  CAS  PubMed  Google Scholar 

  193. Villoutreix Bruno O, Renault Nicolas, Lagorce David et al (2007) Free resources to assist structure-based virtual ligand screening experiments. Curr Protein Pept Sci 8:381–411. https://doi.org/10.2174/138920307781369391

    Article  CAS  PubMed  Google Scholar 

  194. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 64:4–17. https://doi.org/10.1016/j.addr.2012.09.019

    Article  Google Scholar 

  195. Congreve M, Carr R, Murray C, Jhoti H (2003) A “rule of three” for fragment-based lead discovery? Drug Discov Today 8:876–877. https://doi.org/10.1016/s1359-6446(03)02831-9

    Article  PubMed  Google Scholar 

  196. Hughes JD, Blagg J, Price DA et al (2008) Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg Med Chem Lett 18:4872–4875. https://doi.org/10.1016/j.bmcl.2008.07.071

    Article  CAS  PubMed  Google Scholar 

  197. Lagarde N, Zagury J-F, Montes M (2015) Benchmarking data sets for the evaluation of virtual ligand screening methods: review and perspectives. J Chem Inf Model 55:1297–1307. https://doi.org/10.1021/acs.jcim.5b00090

    Article  CAS  PubMed  Google Scholar 

  198. Réau M, Langenfeld F, Zagury J-F et al (2018) Decoys selection in benchmarking datasets: overview and perspectives. Front Pharmacol 9 https://doi.org/10.3389/fphar.2018.00011

  199. Clark RD, Strizhev A, Leonard JM et al (2002) Consensus scoring for ligand/protein interactions. J Mol Graph Model 20:281–295

    Article  CAS  PubMed  Google Scholar 

  200. Cox JAG, Mugumbate G, Del Peral LVG et al (2016) Novel inhibitors of Mycobacterium tuberculosis GuaB2 identified by a target based high-throughput phenotypic screen. Sci Rep 6:1–10. https://doi.org/10.1038/srep38986

    Article  CAS  Google Scholar 

  201. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11:494–502. https://doi.org/10.1016/j.cbpa.2007.08.033

    Article  CAS  PubMed  Google Scholar 

  202. Kumar A, Zhang KYJ (2018) A cross docking pipeline for improving pose prediction and virtual screening performance. J Comput Aided Mol Des 32:163–173. https://doi.org/10.1007/s10822-017-0048-z

    Article  CAS  PubMed  Google Scholar 

  203. Thilagavathi R, Mancera RL (2010) Ligand- protein cross-docking with water molecules. J Chem Inf Model 50:415–421

    Article  CAS  PubMed  Google Scholar 

  204. Kilambi KP, Gray JJ (2017) Structure-based cross-docking analysis of antibody–antigen interactions. Sci Rep 7:8145

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  205. Kroemer RT, Vulpetti A, McDonald JJ et al (2004) Assessment of docking poses: interactions-based accuracy classification (IBAC) versus crystal structure deviations. J Chem Inf Comput Sci 44:871–881

    Article  CAS  PubMed  Google Scholar 

  206. Smith RD, Dunbar JB, Ung PM et al (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51:2115–2131. https://doi.org/10.1021/ci200269q

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Oda A, Tsuchida K, Takakura T et al (2006) Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes. J Chem Inf Model 46:380–391. https://doi.org/10.1021/ci050283k

    Article  CAS  PubMed  Google Scholar 

  208. Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671. https://doi.org/10.1007/s10822-006-9087-6

    Article  CAS  PubMed  Google Scholar 

  209. Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top Med Chem 10:95–115. https://doi.org/10.2174/156802610790232260

    Article  CAS  PubMed  Google Scholar 

  210. Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759. https://doi.org/10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  211. Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50:74–82. https://doi.org/10.1021/jm0603365

    Article  CAS  PubMed  Google Scholar 

  212. Hawkins PCD, Skillman AG, Warren GL et al (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J Chem Inf Model 50:572–584. https://doi.org/10.1021/ci100031x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Takagi T, Amano M, Tomimoto M (2009) Novel method for the evaluation of 3D conformation generators. J Chem Inf Model 49:1377–1388. https://doi.org/10.1021/ci800393w

    Article  CAS  PubMed  Google Scholar 

  214. Ebejer J-P, Morris GM, Deane CM (2012) Freely available conformer generation methods: how good are they? J Chem Inf Model 52:1146–1158. https://doi.org/10.1021/ci2004658

    Article  CAS  PubMed  Google Scholar 

  215. Loferer MJ, Kolossváry I, Aszódi A (2007) Analyzing the performance of conformational search programs on compound databases. J Mol Graph Model 25:700–710. https://doi.org/10.1016/j.jmgm.2006.05.008

    Article  CAS  PubMed  Google Scholar 

  216. Vainio MJ, Johnson MS (2007) Generating conformer ensembles using a multiobjective genetic algorithm. J Chem Inf Model 47:2462–2474. https://doi.org/10.1021/ci6005646

    Article  CAS  PubMed  Google Scholar 

  217. Li J, Ehlers T, Sutter J et al (2007) CAESAR: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration. J Chem Inf Model 47:1923–1932. https://doi.org/10.1021/ci700136x

    Article  CAS  PubMed  Google Scholar 

  218. Watts KS, Dalal P, Murphy RB et al (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50:534–546. https://doi.org/10.1021/ci100015j

    Article  CAS  PubMed  Google Scholar 

  219. O’Boyle N, Vandermeersch T, Hutchison G (2011) Confab—generation of diverse low energy conformers. J Cheminform 3:P32. https://doi.org/10.1186/1758-2946-3-s1-p32

    Article  PubMed Central  Google Scholar 

  220. Gasteiger J, Rudolph C, Sadowski J (1990) Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput Methodol 3:537–547. https://doi.org/10.1016/0898-5529(90)90156-3

    Article  CAS  Google Scholar 

  221. Riniker S, Landrum GA (2015) Better informed distance geometry: using what we know to improve conformation generation. J Chem Inf Model 55:2562–2574. https://doi.org/10.1021/acs.jcim.5b00654

    Article  CAS  PubMed  Google Scholar 

  222. Miteva MA, Guyon F, Tuffery P (2010) Frog2: efficient 3D conformation ensemble generator for small compounds. Nucl Acids Res 38:W622–W627. https://doi.org/10.1093/nar/gkq325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Sauton N, Lagorce D, Villoutreix BO, Miteva MA (2008) MS-DOCK: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening. BMC Bioinform 9:1–12. https://doi.org/10.1186/1471-2105-9-184

    Article  CAS  Google Scholar 

  224. Greg L (2018) RDKit: open-source cheminformatics. http://www.rdkit.org/. Accessed 7 Feb 2018

  225. Sabbadin D, Moro S (2014) Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J Chem Inf Model 54:372–376. https://doi.org/10.1021/ci400766b

    Article  CAS  PubMed  Google Scholar 

  226. Mobley DL, Gilson MK (2017) Predicting binding free energies: frontiers and benchmarks. Annu Rev Biophys 46:531–558. https://doi.org/10.1146/annurev-biophys-070816-033654

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  227. Chodera JD, Mobley DL, Shirts MR et al (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 21:150–160. https://doi.org/10.1016/j.sbi.2011.01.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  228. Shirts MR, Mobley DL, Brown SP (2010) Free-energy calculations in structure-based drug design. In: Merz KM, Ringe D, Reynolds CH (eds) Drug design: structure-and ligand-based approaches. Cambridge University Press, New York, Cambridge, pp 61–86

    Chapter  Google Scholar 

  229. Hansen N, Van Gunsteren WF (2014) Practical aspects of free-energy calculations: a review. J Chem Theory Comput 10:2632–2647. https://doi.org/10.1021/ct500161f

    Article  CAS  PubMed  Google Scholar 

  230. Christ CD, Mark AE, van Gunsteren WF (2009) Basic ingredients of free energy calculations: a review. J Comput Chem 31:1569-1582. https://doi.org/10.1002/jcc.21450

  231. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461. https://doi.org/10.1517/17460441.2015.1032936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  232. Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson-Boltzmann surface area method. Mol Inform 31:114–122. https://doi.org/10.1002/minf.201100135

    Article  CAS  PubMed  Google Scholar 

  233. Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, Cheatham TE III, Darden TA, Duke RE, Gohlke H, Goetz AW, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossváry I, Kovalenko A, Lee TS, LeGrand S, Luchko T, Luo R, Madej B, Merz KM, Paesani F, Roe DR, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM, Wolf X, Kollman PA (2014) Amber14 Reference manual

    Google Scholar 

  234. Sprenger KG, Jaeger VW, Pfaendtner J (2015) The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids. J Phys Chem B 119:5882–5895. https://doi.org/10.1021/acs.jpcb.5b00689

    Article  CAS  PubMed  Google Scholar 

  235. Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713. https://doi.org/10.1021/acs.jctc.5b00255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  236. Nguyen CN, Kurtzman Young T, Gilson MK (2012) Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. J Chem Phys 137:044101. https://doi.org/10.1063/1.4733951

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  237. Kräutler V, Van Gunsteren WF, Hünenberger PH (2001) A fast SHAKE algorithm to solve distance constraint equations for small molecules in molecular dynamics simulations. J Comput Chem 22:501–508. https://doi.org/10.1002/1096-987x(20010415)22:5%3c501:aid-jcc1021%3e3.0.co;2-v

    Article  Google Scholar 

  238. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci 3:198–210. https://doi.org/10.1002/wcms.1121

    Article  CAS  Google Scholar 

  239. Miller BR, McGee TD, Swails JM et al (2012) MMPBSA.py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8:3314–3321. https://doi.org/10.1021/ct300418h

    Article  CAS  PubMed  Google Scholar 

  240. Marlow MS, Dogan J, Frederick KK et al (2010) The role of conformational entropy in molecular recognition by calmodulin. Nat Chem Biol 6:352–358. https://doi.org/10.1038/nchembio.347

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. Kasinath V, Sharp KA, Wand AJ (2013) Microscopic insights into the NMR relaxation-based protein conformational entropy meter. J Am Chem Soc 135:15092–15100. https://doi.org/10.1021/ja405200u

    Article  CAS  PubMed  Google Scholar 

  242. Diehl C, Engström O, Delaine T et al (2010) Protein flexibility and conformational entropy in ligand design targeting the carbohydrate recognition domain of galectin-3. J Am Chem Soc 132:14577–14589. https://doi.org/10.1021/ja105852y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  243. Fenley AT, Muddana HS, Gilson MK (2012) Entropy-enthalpy transduction caused by conformational shifts can obscure the forces driving protein-ligand binding. Proc Natl Acad Sci U S A 109:20006–20011. https://doi.org/10.1073/pnas.1213180109

    Article  CAS  Google Scholar 

  244. Chodera JD, Mobley DL (2013) Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. Annu Rev Biophys 42:121–142. https://doi.org/10.1146/annurev-biophys-083012-130318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  245. Olsson TSG, Ladbury JE, Pitt WR, Williams MA (2011) Extent of enthalpy-entropy compensation in protein-ligand interactions. Protein Sci 20:1607–1618. https://doi.org/10.1002/pro.692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  246. López-Blanco JR, Miyashita O, Tama F, Chacón P (2014) Normal mode analysis techniques in structural biology. In: John Wiley & Sons Ltd (ed) eLS. John Wiley & Sons, Ltd, Chichester, UK, p 9

    Google Scholar 

  247. Numata J, Wan M, Knapp E-W (2007) Conformational entropy of biomolecules: beyond the quasi-harmonic approximation. Genome Inform 18:192–205

    CAS  PubMed  Google Scholar 

  248. Killian BJ, Yundenfreund Kravitz J, Gilson MK (2007) Extraction of configurational entropy from molecular simulations via an expansion approximation. J Chem Phys 127:024107. https://doi.org/10.1063/1.2746329

    Article  CAS  PubMed  Google Scholar 

  249. Numata J, Knapp E-W (2012) Balanced and bias-corrected computation of conformational entropy differences for molecular trajectories. J Chem Theory Comput 8:1235–1245. https://doi.org/10.1021/ct200910z

    Article  CAS  PubMed  Google Scholar 

  250. Suárez E, Díaz N, Méndez J, Suárez D (2013) CENCALC: a computational tool for conformational entropy calculations from molecular simulations. J Comput Chem 34:2041–2054. https://doi.org/10.1002/jcc.23350

    Article  CAS  PubMed  Google Scholar 

  251. Killian BJ, Kravitz JY, Somani S et al (2009) Configurational entropy in protein-peptide binding: computational study of Tsg101 ubiquitin E2 variant domain with an HIV-derived PTAP nonapeptide. J Mol Biol 389:315–335. https://doi.org/10.1016/j.jmb.2009.04.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  252. Fenley AT, Killian BJ, Hnizdo V et al (2014) Correlation as a determinant of configurational entropy in supramolecular and protein systems. J Phys Chem B 118:6447–6455. https://doi.org/10.1021/jp411588b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  253. Fogolari F, Brigo a, Molinari H (2002) The Poisson-Boltzmann equation for biomolecular electrostatics: a tool for structural biology. J Mol Recognit 15:377–392. https://doi.org/10.1002/jmr.577

    Article  CAS  PubMed  Google Scholar 

  254. King BM, Silver NW, Tidor B (2012) Efficient calculation of molecular configurational entropies using an information theoretic approximation. J Phys Chem B 116:2891–2904. https://doi.org/10.1021/jp2068123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  255. Tembre BL, Mc Cammon JA (1984) Ligand-receptor interactions. Comput Chem 8:281–283. https://doi.org/10.1016/0097-8485(84)85020-2

    Article  Google Scholar 

  256. Lybrand TP, Ghosh I, McCammon JA (1985) Hydration of chloride and bromide anions: determination of relative free energy by computer simulation. J Am Chem Soc 107:7793–7794. https://doi.org/10.1021/ja00311a112

    Article  CAS  Google Scholar 

  257. Bash P, Singh U, Langridge R, Kollman P (1987) Free energy calculations by computer simulation. Science 236(80):564–568. https://doi.org/10.1126/science.3576184

    Article  CAS  PubMed  Google Scholar 

  258. Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93:2395–2417. https://doi.org/10.1021/cr00023a004

    Article  CAS  Google Scholar 

  259. Jorgensen WL (1989) Free energy calculations: a breakthrough for modeling organic chemistry in solution. Acc Chem Res 22:184–189. https://doi.org/10.1021/ar00161a004

    Article  CAS  Google Scholar 

  260. Aqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–391

    Article  CAS  PubMed  Google Scholar 

  261. Lee FS, Chu ZT, Bolger MB, Warshel A (1992) Calculations of antibody-antigen interactions: microscopic and semi-microscopic evaluation of the free energies of binding of phosphorylcholine analogs to McPC603. Protein Eng 5:215–228. https://doi.org/10.1093/protein/5.3.215

    Article  CAS  PubMed  Google Scholar 

  262. Ermak DL, McCammon JA (1978) Brownian dynamics with hydrodynamic interactions. J Chem Phys 69:1352. https://doi.org/10.1063/1.436761

    Article  CAS  Google Scholar 

  263. Adcock SA, McCammon JA (2006) Molecular dynamics: survey of methods for simulating the activity of proteins. Chem Rev 106:1589–1615. https://doi.org/10.1021/cr040426m

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  264. Bek S, Jakobsson E (1994) Brownian dynamics study of a multiply-occupied cation channel: application to understanding permeation in potassium channels. Biophys J 66:1028–1038. https://doi.org/10.1016/s0006-3495(94)80884-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  265. Sines J, Allison S, McCammon JA (1990) Brownian dynamics simulation of the superoxide-superoxide dismutase reaction: iron and manganese enzymes. J Phys Chem 94:959–961

    Article  CAS  Google Scholar 

  266. Northrup SH, Erickson HP (1992) Kinetics of protein–protein association explained by Brownian dynamics computer simulation. Proc Natl Acad Sci U S A 89:3338–3342

    Article  CAS  Google Scholar 

  267. Kozack RE, Subramaniam S (1993) Brownian dynamics simulations of molecular recognition in an antibody-antigen system. Protein Sci 2:915–926. https://doi.org/10.1002/pro.5560020605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  268. Gabdoulline RR, Wade RC (1997) Simulation of the diffusional association of barnase and barstar. Biophys J 72:1917–1929. https://doi.org/10.1016/s0006-3495(97)78838-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  269. Gabdoulline RR, Wade RC (1998) Brownian dynamics simulation of protein–protein diffusional encounter. Methods 14:329–341. https://doi.org/10.1006/meth.1998.0588

    Article  CAS  PubMed  Google Scholar 

  270. Deganutti G, Cuzzolin A, Ciancetta A, Moro S (2015) Understanding allosteric interactions in G protein-coupled receptors using supervised molecular dynamics: a prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000. Bioorg Med Chem 23:4065–4071. https://doi.org/10.1016/j.bmc.2015.03.039

    Article  CAS  PubMed  Google Scholar 

  271. Cuzzolin A, Sturlese M, Deganutti G et al (2016) Deciphering the complexity of ligand-protein recognition pathways using supervised molecular dynamics (SuMD) simulations. J Chem Inf Model 56:687–705. https://doi.org/10.1021/acs.jcim.5b00702

    Article  CAS  PubMed  Google Scholar 

  272. Paoletta S, Sabbadin D, von Kügelgen I et al (2015) Modeling ligand recognition at the P2Y12 receptor in light of X-ray structural information. J Comput Aided Mol Des 29:737–756. https://doi.org/10.1007/s10822-015-9858-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  273. Lin JH, Lu AYH (1997) Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol Rev 49:403–449

    CAS  PubMed  Google Scholar 

  274. Gallo JM (2010) Pharmacokinetic/pharmacodynamic-driven drug development. Mt Sinai J Med A J Transl Pers Med 77:381–388. https://doi.org/10.1002/msj.20193

    Article  Google Scholar 

  275. Alavijeh MS, Chishty M, Qaiser MZ, Palmer AM (2005) Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery. NeuroRx 2:554–571. https://doi.org/10.1602/neurorx.2.4.554

    Article  PubMed  PubMed Central  Google Scholar 

  276. Altshuler J, Flanagan A, Guy P et al (2001) A revolution in R&D: how genomics and genetics are transforming the biopharmaceutical industry. Boston Consulting Group, Boston

    Google Scholar 

  277. Davis AM, Riley RJ (2004) Predictive ADMET studies, the challenges and the opportunities. Curr Opin Chem Biol 8:378–386

    Article  CAS  PubMed  Google Scholar 

  278. White RE (1998) Short-and long-term projections about the use of drug metabolism in drug discovery and development. Drug Metab Dispos 26:1213–1216

    CAS  PubMed  Google Scholar 

  279. Eddershaw PJ, Beresford AP, Bayliss MK (2000) ADME/PK as part of a rational approach to drug discovery. Drug Discov Today 5:409–414

    Article  CAS  PubMed  Google Scholar 

  280. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711

    Article  CAS  PubMed  Google Scholar 

  281. Harris CJ, Hill RD, Sheppard DW et al (2011) The design and application of target-focused compound libraries. Comb Chem High Throughput Screen 14:521–531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  282. Paricharak S, Méndez-Lucio O, Chavan Ravindranath A et al (2016) Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening. Brief Bioinform 19(2):277–285. https://doi.org/10.1093/bib/bbw105

    Article  PubMed Central  Google Scholar 

  283. Chuprina A, Lukin O, Demoiseaux R et al (2010) Drug-and lead-likeness, target class, and molecular diversity analysis of 7.9 million commercially available organic compounds provided by 29 suppliers. J Chem Inf Model 50:470–479

    Article  CAS  PubMed  Google Scholar 

  284. Oprea TI, Allu TK, Fara DC et al (2007) Lead-like, drug-like or “Pub-like”: how different are they? J Comput Aided Mol Des 21:113–119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  285. Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719–2740. https://doi.org/10.1021/jm901137j

    Article  CAS  PubMed  Google Scholar 

  286. Metz JT, Huth JR, Hajduk PJ (2007) Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J Comput Aided Mol Des 21:139–144. https://doi.org/10.1007/s10822-007-9109-z

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We sincerely thank the facilities under Center of Excellence and Builder project, Department of Biotechnology (DBT), for supporting the computational work and DST purse for software support. SKP is supported by DBT-BINC fellowship. The authors thank all group members and Pawan Kumar for valuable input on improving the manuscript.

Author information

Authors and Affiliations

  1. School of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi, 110067, India

    Shailesh Kumar Panday & Indira Ghosh

Authors
  1. Shailesh Kumar Panday
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Indira Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Indira Ghosh .

Editor information

Editors and Affiliations

  1. Amrita Centre for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Kochi, India

    C. Gopi Mohan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Panday, S.K., Ghosh, I. (2019). In Silico Structure-Based Prediction of Receptor–Ligand Binding Affinity: Current Progress and Challenges. In: Mohan, C. (eds) Structural Bioinformatics: Applications in Preclinical Drug Discovery Process. Challenges and Advances in Computational Chemistry and Physics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-030-05282-9_5

Download citation

Publish with us

Policies and ethics

Search

Navigation