Skip to main content

De Novo Design of Ligands Using Computational Methods

  • Protocol
  • First Online:
Book cover Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1762))

Abstract

De novo design technique is complementary to high-throughput virtual screening and is believed to contribute in pharmaceutical development of novel drugs with desired properties at a very low cost and time-efficient manner. In this chapter, we outline the basic de novo design concepts based on computational methods with an example.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
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

Institutional subscriptions

References

  1. Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591

    Article  CAS  PubMed  Google Scholar 

  2. Clark DE, Pickett SD (2000) Computational methods for the prediction of 'drug-likeness'. Drug Discov Today 5:49–58

    Article  CAS  PubMed  Google Scholar 

  3. Loving K, Alberts I, Sherman W (2010) Computational approaches for fragment-based and de novo design. Curr Top Med Chem 10:14–32

    Article  CAS  PubMed  Google Scholar 

  4. Moon JB, Howe WJ (1991) Computer design of bioactive molecules: a method for receptor-based de novo ligand design. Proteins 11:314–328

    Article  CAS  PubMed  Google Scholar 

  5. Joseph-McCarthy D (1999) Computational approaches to structure-based ligand design. Pharmacol Ther 84:179–191

    Article  CAS  PubMed  Google Scholar 

  6. Aparoy P, Reddy KK, Reddanna P (2012) Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem 19:3763–3778

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Po-Ssu H, Boyken SE, Baker D (2016) The coming of age of de novo protein design. Nature 537:320–327

    Article  Google Scholar 

  8. Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4:649–663

    Article  CAS  PubMed  Google Scholar 

  9. Hartenfeller M, Schneider G (2011) Enabling future drug discovery by de novo design. Wiley Interdiscip Rev Comput Mol Sci 1:742–759

    Article  CAS  Google Scholar 

  10. Butina D, Segall MD, Frankcombe K (2002) Predicting ADME properties in silico: methods and models. Drug Discov Today 7:S83–S88

    Article  CAS  PubMed  Google Scholar 

  11. Takeda S, Kaneko H, Funatsu K (2016) Chemical-space-based de novo design method to generate drug-like molecules. J Chem Inf Model 56:1885–1893

    Article  CAS  PubMed  Google Scholar 

  12. Jain SK, Agrawal A, Stahl M, Schneider P (2004) De novo drug design: an overview. Indian J Pharm Sci 66:721–728

    CAS  Google Scholar 

  13. Hilpert K, Ackermann J, Banner DW, Gast A, Gubernator K, Hadváry P, Labler L, Müller K, Schmid G, Tschopp TB, Waterbeemd HVD (1994) Design and synthesis of potent and highly selective thrombin inhibitors. J Med Chem 37:3889–3901

    Article  CAS  PubMed  Google Scholar 

  14. Webber SE, Bleckman TM, Attard J, Deal JG, Kathardekar V, Welsh KM, Webber S, Janson CA, Matthews DA, Smith WW, Freer ST, Jordan SR, Bacquet RJ, Howland EF, Booth CLJ, Ward RW, Hermann SM, White J, Morse CA, Hilliard JA, Bartlett CA (1993) Design of thymidylate synthase inhibitors using protein crystal structures: the synthesis and biological evaluation of a novel class of 5-substituted quinazolinones. J Med Chem 36:733–746

    Article  CAS  PubMed  Google Scholar 

  15. Greer J, Erickson JW, Baldwin JJ, Varney MD (1994) Application of the three-dimensional structures of protein target molecules in structure-based drug design. J Med Chem 37:1035–1054

    Article  CAS  PubMed  Google Scholar 

  16. Baldwin JJ, Ponticello GS, Anderson PS, Christy ME, Murcko MA, Randall WC, Schwam H, Sugrue MF, Springer JP, Gautheron P, Grove J, Mallorga P, Viadert MP, McKeever BM, Navia MA (1989) Thienothiopyran-2-sulfonamides: novel topically active carbonic anhydrase inhibitors for the treatment of glaucoma. J Med Chem 32:2510–2513

    Article  CAS  PubMed  Google Scholar 

  17. Verlinde CL, Callens M, Van Calenbergh S, Van Aerschot A, Herdewijn P, Hannaert V, Michels PA, Opperdoes FR, Hol WG (1994) Selective inhibition of trypanosomal glyceraldehyde-3-phosphate dehydrogenase by protein structure-based design: toward new drugs for the treatment of sleeping sickness. J Med Chem 37:3605–3613

    Article  CAS  PubMed  Google Scholar 

  18. Von Itzstein M, Wu WY, Kok GB, Pegg MS, Dyason JC, Jin B, Van Phan T, Smythe ML, White HF, Oliver SW, Colman PM, Varghese JN, Ryan DM, Woods JM, Bethell RC, Hotham VJ, Cameron JM, Penn CR (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363:418–423

    Article  Google Scholar 

  19. Diana GD, Treasurywala AM, Bailey TR, Oglesby RC, Pevear DC, Dutko FJ (1990) A model for compounds active against human Rhinovirus-14 based on X-ray crystallography data. J Med Chem 33:1306–1311

    Article  CAS  PubMed  Google Scholar 

  20. Diana GD, Treasurywala A (1991) Design of compounds active against HRV-14. Drug News Perspect 4:517–523

    Google Scholar 

  21. Schmidt JM, Mercure J, Tremblay GB, Pagé M, Kalbakji A, Feher M, Dunn-Dufault R, Peter MG, Redden PR (2003) De novo design, synthesis, and evaluation of novel nonsteroidal phenanthrene ligands for the estrogen receptor. J Med Chem 46:1408–1418

    Article  CAS  PubMed  Google Scholar 

  22. Haitao Ji et al (2003) Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for Lanosterol 14α-Demethylase of fungi. J Med Chem 46:474–485

    Google Scholar 

  23. Liu YZ, Wang XL, Wang XY, Yu RL, Liu DQ, Kang CM (2016) De novo design of VEGFR-2 tyrosine kinase inhibitors based on a linked-fragment approach. J Mol Model 22:222

    Google Scholar 

  24. Kankanala J, Latham AM, Johnson AP, Homer-Vanniasinkam S, Fishwick CW, Ponnambalam S (2012) A combinatorial in silico and cellular approach to identify a new class of compounds that target VEGFR2 receptor tyrosine kinase activity and angiogenesis. Br J Pharmacol 166:737–748

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Böhm HJ, Banner DW, Weber L (1999) Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J Comput Aided Mol Des 13:51–56

    Article  PubMed  Google Scholar 

  26. Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, P38, and HIV protease. J Med Chem 47:2768–2775

    Article  CAS  PubMed  Google Scholar 

  27. Rogers-Evans M, Alanine AI, Bleicher KH, Kube D, Schneider G (2004) Identification of novel cannabinoid receptor ligands via evolutionary de novo design and rapid parallel synthesis. QSAR Comb Sci 23:426–430

    Article  CAS  Google Scholar 

  28. Huang Q, Li LL, Sheng-Yong Y (2010) PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility. J Mol Graph Model 28:775–787

    Article  CAS  PubMed  Google Scholar 

  29. Heikkilä T, Thirumalairajan S, Davies M, Parsons MR, McConkey AG, Fishwick CWG, Johnson AP (2006) The first de novo designed inhibitors of plasmodium falciparum dihydroorotate dehydrogenase. Bioorg Med Chem Lett 16:88–92

    Article  PubMed  Google Scholar 

  30. Babine RE, Bleckman TM, Kissinger CR, Showalter R, Pelletier LA, Lewis C, Tucker K, Moomaw E, Parge HE, Villafranca JE (1995) Design synthesis and X-ray crystallographic studies of novel FKBP-12 ligand. Bioorg Med Chem Lett 5:1719–1724

    Article  CAS  Google Scholar 

  31. Heartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Wegen S, Stark H, Schneider G (2012) DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8:1–12

    Google Scholar 

  32. Proschak E, Sander K, Zettl H, Tanrikulu Y, Rau O, Schneider P, Schubert-Zsilavecz M, Stark H, Schneider G (2009) From molecular shape to potent bioactive agents II: fragment-based de novo design. ChemMedChem 4:45–48

    Article  CAS  PubMed  Google Scholar 

  33. Dhanjal JK, Sreenidhi AK, Bafna K, Katiyar SP, Goyal S, Grover A, Sundar D (2015) Computational structure-based de novo design of hypothetical inhibitors against the anti-inflammatory target COX-2. PLoS One 10:e0134691

    Article  PubMed  PubMed Central  Google Scholar 

  34. Damewood JR, Lerman CL, Masek BB (2010) NovoFLAP: a ligand-based de novo design approach for the generation of medicinally relevant ideas. J Chem Inf Model 50:1296–1303

    Article  CAS  PubMed  Google Scholar 

  35. Danziger DJ, Dean PM (1989) Automated site-directed drug design: a general algorithm for knowledge acquisition about hydrogen bonding regions at protein surfaces. Proc R Soc Lond B Biol Sci 236:101–113

    Article  CAS  PubMed  Google Scholar 

  36. Lewis RA (1990) Automated site-directed drug design: approaches to the formation of 3D molecular graphs. J Comput Aided Mol Des 4:205–210

    Article  CAS  PubMed  Google Scholar 

  37. Nishibata Y, Itai A (1991) Automatic creation of dug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 47:8985–8990

    Article  CAS  Google Scholar 

  38. Lewis RA, Roe DC, Huang C, Ferrin TE, Langridge R, Kuntz ID (1992) Automated site-directed drug design using molecular lattices. J Mol Graph 10:66–78

    Article  CAS  PubMed  Google Scholar 

  39. Böhm HJ (1992) The computer program LUDI: a new simple method for the de-novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78

    Article  PubMed  Google Scholar 

  40. Tschinke V, Cohen NC (1993) The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypothesis. J Med Chem 36:3863–3870

    Article  CAS  PubMed  Google Scholar 

  41. Rotstein SH, Murcko MA (1993) Group build: a fragment-based method for de novo drug design. J Med Chem 36:1700–1710

    Article  CAS  PubMed  Google Scholar 

  42. Ho CMW, Marshall GR (1993) SPLICE: a program to assemble partial query solutions from three-dimensional database searches into novel ligands. J Comput Aided Mol Des 7:623–647

    Article  CAS  Google Scholar 

  43. Rotstein SH, Murcko MA (1993) GenStar: a method for de novo drug design. J Comput Aided Mol Des 7:23–43

    Article  CAS  PubMed  Google Scholar 

  44. Pearlman DA, Murcko MA (1993) CONCEPTS: new dynamic algorithm for de novo design suggestion. J Comput Chem 14:1184–1193

    Article  CAS  Google Scholar 

  45. Gillett VJ, Myatt G, Zsoldos Z, Johnson AP (1995) SPROUT, HIPPO and CAESA: tools for de novo structure generation and estimation of synthetic accessibility. Perspect Drug Discov Des 3:34–50

    Article  Google Scholar 

  46. Eisen MB, Wiley DC, Karplus M, Hubbard RE (1994) HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins 19:199–221

    Article  CAS  PubMed  Google Scholar 

  47. Bohacek RS, McMartin C (1994) Multiple highly diverse structures complementary to enzyme binding sites: results of extensive application of a de novo design method incorporating combinatorial growth. J Am Chem Soc 116:5560–5571

    Article  CAS  Google Scholar 

  48. Glen RC, Payne AWR (1995) A genetic algorithm for the automated generation of molecules within constraints. J Comput Aided Mol Des 9:181–202

    Article  CAS  PubMed  Google Scholar 

  49. Clark DE, Frenkel D, Levy SA, Li J, Murray CW, Robson B, Waszkowycz B, Westhead DR (1995) PRO-LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules. J Comput Aided Mol Des 9:13–32

    Article  CAS  PubMed  Google Scholar 

  50. Miranker A, Karplus M (1995) An automated method for dynamic ligand design. Proteins 23:472–490

    Article  CAS  PubMed  Google Scholar 

  51. DeWitte RS, Shakhnovich EI (1996) SMoG de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 118:11733–11744

    Article  CAS  Google Scholar 

  52. Pearlman DA, Murcko MA (1996) CONCERTS: dynamic connection of fragments as an approach to de novo ligand design. J Med Chem 39:1651–1663

    Article  CAS  PubMed  Google Scholar 

  53. Luo Z, Wang R, Lai L (1996) RASSE: a new method for structure-based drug design. J Chem Inf Comput Sci 36:1187–1194

    Article  CAS  PubMed  Google Scholar 

  54. Murray CW, Clark DE, Auton TR, Firth MA, Li J, Sykes RA, Waszkowycz B, Westhead DR, Young SC (1997) PRO_SELECT: combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology. J Comput Aided Mol Des 11:193–207

    Article  CAS  PubMed  Google Scholar 

  55. Todorov NP, Dean PM (1997) Evaluation of a method for controlling molecular scaffold diversity in de novo ligand design. J Comput Aided Mol Des 11:175–192

    Article  CAS  PubMed  Google Scholar 

  56. Nachbar RB (2000) Molecular evolution: automated manipulation of hierarchical chemical topology and its application to average molecular structures. Genet Program Evolvable Mach 1:57–94

    Article  Google Scholar 

  57. Globus A, Lawton J, Wipke WT (1999) Automatic molecular design using evolutionary algorithms. Nanotechnology 10:290–299

    Article  Google Scholar 

  58. Liu H, Duan Z, Luo Q, Shi Y (1999) Structure based ligand design by dynamically assembling molecular building blocks at binding site. Proteins 36:462–470

    Article  CAS  PubMed  Google Scholar 

  59. Douguet D, Thoreau E, Grassy G (2000) A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm. J Comput Aided Mol Des 14:449–466

    Article  CAS  PubMed  Google Scholar 

  60. Wang R, Gao Y, Lai L (2000) LigBuilder: a multi-purpose program for structure-based drug design. J Mol Model 6:498–516

    Article  CAS  Google Scholar 

  61. Schneider G, Lee ML, Stahl M, Schneider P (2000) De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J Comput Aided Mol Des 14:487–494

    Article  CAS  PubMed  Google Scholar 

  62. Zhu J, Fan H, Liu H, Shi Y (2001) Structure based ligand design for flexible proteins: application of new F-Dyco block. J Comput Aided Mol Des 15:979–996

    Article  CAS  PubMed  Google Scholar 

  63. Pegg SCH, Haresco JJ, Kuntz ID (2001) A genetic algorithm for structure-based de novo design. J Comput Aided Mol Des 15:911–933

    Article  CAS  PubMed  Google Scholar 

  64. Pellegrini E, Field MJ (2003) Development and testing of a de novo drug-design algorithm. J Comput Aided Mol Des 17:621–641

    Article  CAS  PubMed  Google Scholar 

  65. Vinkers HM, de Jonge MR, Daeyaert FF, Heeres J, Koymans LM, van Lenthe JH, Lewi PJ, Timmerman H, Van Aken K, Janssen PA (2003) SYNOPSIS: SYNthesize and OPtimize system in silico. J Med Chem 46:2765–2773

    Article  CAS  PubMed  Google Scholar 

  66. Brown N, McKay B, Gilardoni F, Gasteiger J (2004) A graph-based genetic algorithm and its application to the multi objective evolution of median molecules. J Chem Inf Comput Sci 44:1079–1087

    Article  CAS  PubMed  Google Scholar 

  67. Nikitin S, Zaitseva N, Demina O, Solovieva V, Mazin E, Mikhalev S, Smolov M, Rubinov A, Vlasov P, Lepikhin D, Khachko D, Fokin V, Queen C, Zosimov V (2005) A very large diversity space of synthetically accessible compounds for use with drug design programs. J Comput Aided Mol Des 19:47–63

    Article  CAS  PubMed  Google Scholar 

  68. Douguet D, Munier-Lehmann H, Labesse G, Pochet S (2005) LEA3D: a computer-aided ligand design for structure-based drug design. J Med Chem 48:2457–2468

    Article  CAS  PubMed  Google Scholar 

  69. Fechner U, Schneider G (2006) Flux (1): a virtual synthesis scheme for fragment based de novo design. J Chem Inf Model 46:699–707

    Article  CAS  PubMed  Google Scholar 

  70. Fechner U, Schneider G (2007) Flux (2): comparison of molecular mutation and crossover operators for ligand-based de novo design. J Chem Inf Model 47:656–667

    Article  CAS  PubMed  Google Scholar 

  71. Dey F, Cafl isch A (2008) Fragment-based de novo ligand design by multi objective evolutionary optimization. J Chem Inf Model 48:679–690

    Article  CAS  PubMed  Google Scholar 

  72. Hartenfeller M, Proschak E, Schüller A, Schneider G (2008) Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization. Chem Biol Drug Des 72:16–26

    Article  CAS  PubMed  Google Scholar 

  73. Hecht D, Fogel GB (2009) Novel in silico approach to drug discovery via computational intelligence. J Chem Inf Model 49:1105–1121

    Article  CAS  PubMed  Google Scholar 

  74. Moriaud F, Doppelt-Azeroual O, Martin L, Oguievetskaia K, Koch K, Vorotyntsev A, Adcock SA, Delfaud F (2009) Computational fragment-based approach at PDB scale by protein local similarity. J Chem Inf Model 49:280–294

    Article  CAS  PubMed  Google Scholar 

  75. Nicolaou CA, Apostolakis J, Pattichis CS (2009) De novo drug design using multiobjective evolutionary graphs. J Chem Inf Model 49:295–307

    Article  CAS  PubMed  Google Scholar 

  76. Durrant JD, Amaro RE, McCammon JA (2009) AutoGrow: a novel algorithm for protein inhibitor design. Chem Biol Drug Des 73:168–178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. TY T, Chang KW, Chen CY (2011) iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des 25:525–531

    Article  Google Scholar 

  78. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  PubMed  Google Scholar 

  79. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A (2007) Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 50(2.9):2.9.1–2.9.31

    Article  Google Scholar 

  80. Wang R, Liu L, Lai L, Tang Y (1998) SCORE: a new empirical method for estimating the binding affinity of a protein-ligand complex. Mol Model Ann 4:379–394

    Article  CAS  Google Scholar 

  81. Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO (2008) FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinformatics 9:396

    Article  PubMed  PubMed Central  Google Scholar 

  82. Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G (2013) Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for‘orphan’molecules. Mol Inform 32:133–138

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Hess B, Kutzner C, Van Der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447

    Article  CAS  PubMed  Google Scholar 

  85. Schrödinger Release 2017–1: SiteMap, Schrödinger, LLC, New York, NY, 2017

    Google Scholar 

  86. Selvaraj C, Priya RB, Lee JK, Singh SK (2015) Mechanistic insights of SrtA-LPXTG blockers targeting the transpeptidase mechanism in Streptococcus mutans. RSC Adv 5:100498–100510

    Article  CAS  Google Scholar 

  87. Yang J, Roy A, Zhang Y (2013) Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 29:2588–2595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Singh S, Prabhu SV, Suryanarayanan V, Bhardwaj R, Singh SK, Dubey VK (2016) Molecular docking and structure based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani. J Biomol Struct Dyn 34(11):2367–2386

    Article  CAS  PubMed  Google Scholar 

  89. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J Comput Aid Mol Des 27:221–234

    Article  Google Scholar 

  90. Bhattacharya D, Nowotny J, Cao R, Cheng J (2016) 3Drefine: an interactive web server for efficient protein structure refinement. Nucleic Acids Res 44:W406–W409

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Reddy KK, Singh SK (2015) Insight into the binding mode between N-methyl Pyrimidones and prototype foamy virus integrase-DNA complex by QM-polarized ligand docking and molecular dynamics simulations. Curr Top Med Chem 15:43–49

    Article  CAS  PubMed  Google Scholar 

  92. Aarthy M, Panwar U, Selvaraj C, Singh SK (2017) Advantages of structure-based drug design approaches in neurological disorders. Curr Neuropharmacol 15(8):1136–1155. https://doi.org/10.2174/1570159X15666170102145257

    Article  CAS  PubMed  Google Scholar 

  93. Schrödinger Release 2017–1: QikProp, Schrödinger, LLC, New York, NY, 2017

    Google Scholar 

  94. Mombelli E (2008) An evaluation of the predictive ability of the QSAR software packages, DEREK, HAZARDEXPERT and TOPKAT, to describe chemically-induced skin irritation. Altern Lab Anim 36:15–24

    CAS  PubMed  Google Scholar 

  95. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717

    Article  PubMed  PubMed Central  Google Scholar 

  96. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, Lee PW, Tang Y (2012) admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 52:3099–3105

    Article  CAS  PubMed  Google Scholar 

  97. Suryanarayanan V, Singh SK (2015) Assessment of dual inhibition property of newly discovered inhibitors against PCAF and GCN5 through insilico screening, molecular dynamics simulation and DFT approach. J Recept Signal Transduct Res 35:370–380

    Article  CAS  PubMed  Google Scholar 

  98. Fukunishi Y, Kurosawa T, Mikami Y, Hv N (2014) Prediction of synthetic accessibility based on commercially available compound databases. J Chem Inf Model 54:3259–3267

    Article  CAS  PubMed  Google Scholar 

  99. Genheden S, Ulf R (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, Philipp DM, Rinaldo D, Halls MD, Zhang J, Friesner RA (2013) Jaguar: a high-performance quantum chemistry software program with strengths in life and materials sciences. Int J Quantum Chem 113:2110–2142

    Article  CAS  Google Scholar 

Download references

Acknowledgment

SKS thanks Department of Biotechnology (DBT), New Delhi for providing financial support. VS and UP gratefully acknowledge DST (New Delhi) for INSPIRE Senior Research Fellowship (No. DST/INSPIRE Fellowship/2012/482) and Alagappa University for AURF (No. Ph.D./1122/AURF FELLOWSHIP/2015) respectively.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Suryanarayanan, V., Panwar, U., Chandra, I., Singh, S.K. (2018). De Novo Design of Ligands Using Computational Methods. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7756-7_5

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7755-0

  • Online ISBN: 978-1-4939-7756-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics