Skip to main content

Molecular Scaffold Hopping via Holistic Molecular Representation

  • Protocol
  • First Online:
Protein-Ligand Interactions and Drug Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2266))

Abstract

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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

Institutional subscriptions

References

  1. Schneider G, Neidhart W, Giller T et al (1999) “Scaffold-Hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed 38:2894–2896

    Google Scholar 

  2. Teuber L, Watjen F, Jensen L (1999) Ligands for the benzodiazepine binding site-a survey. Curr Pharm Des 5:317–344

    Article  CAS  PubMed  Google Scholar 

  3. Patel S, Harris SF, Gibbons P et al (2015) Scaffold-hopping and structure-based discovery of potent, selective, and brain penetrant N-(1H-Pyrazol-3-yl)pyridin-2-amine inhibitors of dual leucine zipper kinase (DLK, MAP3K12). J Med Chem 58:8182–8199

    Article  CAS  PubMed  Google Scholar 

  4. Jiang Z, Liu N, Dong G et al (2014) Scaffold hopping of sampangine: discovery of potent antifungal lead compound against Aspergillus fumigatus and Cryptococcus neoformans. Bioorg Med Chem Lett 24:4090–4094

    Article  CAS  PubMed  Google Scholar 

  5. Olson GL, Bolin DR, Bonner MP et al (1993) Concepts and progress in the development of peptide mimetics. J Med Chem 36:3039–3049

    Article  CAS  PubMed  Google Scholar 

  6. Friedrich L, Rodrigues T, Neuhaus CS et al (2016) From complex natural products to simple synthetic mimetics by computational de novo design. Angew Chem Int Ed 55:6789–6792

    Article  CAS  Google Scholar 

  7. Tresadern G, Cid JM, Macdonald GJ et al (2010) Scaffold hopping from pyridones to imidazo[1,2-a]pyridines. New positive allosteric modulators of metabotropic glutamate 2 receptor. Bioorg Med Chem Lett 20:175–179

    Article  CAS  PubMed  Google Scholar 

  8. Yang H, Sun L, Wang Z et al (2018) ADMETopt: a web server for ADMET optimization in drug design via scaffold hopping. J Chem Inf Model 58:2051–2056

    Article  CAS  PubMed  Google Scholar 

  9. Böhm H-J, Flohr A, Stahl M (2004) Scaffold hopping. Drug Discov Today Technol 1:217–224

    Article  PubMed  CAS  Google Scholar 

  10. Taylor RD, MacCoss M, Lawson ADG (2014) Rings in drugs. J Med Chem 57:5845–5859

    Article  CAS  PubMed  Google Scholar 

  11. Hessler G, Baringhaus K-H (2010) The scaffold hopping potential of pharmacophores. Drug Discov Today Technol 7:e263–e269

    Article  CAS  Google Scholar 

  12. Lauri G, Bartlett PA (1994) CAVEAT: a program to facilitate the design of organic molecules. J Comput Aided Mol Des 8:51–66

    Article  CAS  PubMed  Google Scholar 

  13. Maass P, Schulz-Gasch T, Stahl M et al (2007) Recore: a fast and versatile method for scaffold hopping based on small molecule crystal structure conformations. J Chem Inf Model 47:390–399

    Article  CAS  PubMed  Google Scholar 

  14. Bergmann R, Linusson A, Zamora I (2007) SHOP: scaffold HOPping by GRID-based similarity searches. J Med Chem 50:2708–2717

    Article  CAS  PubMed  Google Scholar 

  15. Zhang Q, Muegge I (2006) Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J Med Chem 49:1536–1548

    Article  CAS  PubMed  Google Scholar 

  16. Vogt M, Stumpfe D, Geppert H et al (2010) Scaffold hopping using two-dimensional fingerprints: true potential, black magic, or a hopeless endeavor? Guidelines for virtual screening. J Med Chem 53:5707–5715

    Article  CAS  PubMed  Google Scholar 

  17. Merk D, Grisoni F, Friedrich L et al (2018) Scaffold hopping from synthetic RXR modulators by virtual screening and de novo design. Med Chem Comm 9:1289–1292

    Article  CAS  Google Scholar 

  18. Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley

    Google Scholar 

  19. Maggiora G, Vogt M, Stumpfe D et al (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186–3204

    Article  CAS  PubMed  Google Scholar 

  20. Schneider G, Schneider P, Renner S (2006) Scaffold-hopping: how far can you jump? QSAR Comb Sci 25:1162–1171

    Article  CAS  Google Scholar 

  21. Stumpfe D, Hu Y, Dimova D et al (2014) Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem 57:18–28

    Article  CAS  PubMed  Google Scholar 

  22. Maggiora GM (2006) On outliers and activity cliffs – why QSAR often disappoints. J Chem Inf Model 46:1535–1535

    Article  CAS  PubMed  Google Scholar 

  23. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics: volume I: alphabetical listing / volume II: appendices, references. John Wiley & Sons

    Google Scholar 

  24. Bajorath J (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41:233–245

    Article  CAS  PubMed  Google Scholar 

  25. Pozzan A (2006) Molecular descriptors and methods for ligand based virtual high throughput screening in drug discovery. Curr Pharm Des 12:2099–2110

    Article  CAS  PubMed  Google Scholar 

  26. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046–1053

    Article  CAS  PubMed  Google Scholar 

  27. Cereto-Massagué A, Ojeda MJ, Valls C et al (2015) Molecular fingerprint similarity search in virtual screening. Virtual Screen 71:58–63

    Google Scholar 

  28. Grisoni F, Consonni V, Todeschini R (2018) Impact of molecular descriptors on computational models. In: Brown JB (ed) Computational Chemogenomics. Springer, New York, NY, pp 171–209

    Chapter  Google Scholar 

  29. Arimoto R, Prasad M-A, Gifford EM (2005) Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors. J Biomol Screen 10:197–205

    Article  CAS  PubMed  Google Scholar 

  30. Lv W, Xue Y (2010) Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. Eur J Med Chem 45:1167–1172

    Article  CAS  PubMed  Google Scholar 

  31. Redkar S, Mondal S, Joseph A et al (2020) A machine learning approach for drug-target interaction prediction using wrapper feature selection and class balancing. Mol Inf 39:1900062. https://doi.org/10.1002/minf.201900062

    Article  CAS  Google Scholar 

  32. Zhang H, Liu C-T, Mao J et al (2020) Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach. Toxicol In Vitro 65:104812

    Article  CAS  PubMed  Google Scholar 

  33. Grisoni F, Ballabio D, Todeschini R et al (2018) Molecular descriptors for structure–activity applications: a hands-on approach. In: Nicolotti O (ed) Computational toxicology: methods and protocols. Springer, New York, NY, pp 3–53

    Chapter  Google Scholar 

  34. Willett P (2014) The calculation of molecular structural similarity: principles and practice. Mol Inf 33:403–413

    Article  CAS  Google Scholar 

  35. Todeschini R, Ballabio D, Consonni V (2015) Distances and other dissimilarity measures in chemometrics. In: Encyclopedia of Analytical Chemistry. John Wiley & Sons, Ltd

    Google Scholar 

  36. Grisoni F, Reker D, Schneider P et al (2017) Matrix-based molecular descriptors for prospective virtual compound screening. Mol Inf 36:1600091

    Article  CAS  Google Scholar 

  37. Rivera-Borroto OM, Marrero-Ponce Y, García-de la Vega JM et al (2011) Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors. J Chem Inf Model 51:3036–3049

    Article  CAS  PubMed  Google Scholar 

  38. Li H, Yap CW, Ung CY et al (2005) Effect of selection of molecular descriptors on the prediction of blood−brain barrier penetrating and nonpenetrating agents by statistical learning methods. J Chem Inf Model 45:1376–1384

    Article  CAS  PubMed  Google Scholar 

  39. Schneider P, Schneider G (2016) De novo design at the edge of chaos. J Med Chem 59:4077–4086

    Article  CAS  PubMed  Google Scholar 

  40. Grisoni F, Consonni V, Ballabio D (2019) Machine learning consensus to predict the binding to the androgen receptor within the CoMPARA project. J Chem Inf Model 59:1839–1848

    Article  CAS  PubMed  Google Scholar 

  41. Medina-Franco JL, Martínez-Mayorga K, Bender A et al (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49:477–491

    Article  CAS  PubMed  Google Scholar 

  42. Tetko IV, Sushko I, Pandey AK et al (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 48:1733–1746

    Article  CAS  PubMed  Google Scholar 

  43. Zhu H, Tropsha A, Fourches D et al (2008) Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J Chem Inf Model 48:766–784

    Article  CAS  PubMed  Google Scholar 

  44. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics (2 volumes). Wiley-VCH

    Google Scholar 

  45. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754

    Article  CAS  PubMed  Google Scholar 

  46. Kearnes S, McCloskey K, Berndl M et al (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30:595–608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–692

    Article  CAS  PubMed  Google Scholar 

  48. Todeschini R, Lasagni M, Marengo E (1994) New molecular descriptors for 2D and 3D structures. Theory. J Chemom 8:263–272

    Article  CAS  Google Scholar 

  49. Moriguchi I, HIRONO S, LIU Q et al (1992) Simple method of calculating octanol/water partition coefficient. Chem Pharm Bull (Tokyo) 40:127–130

    Article  CAS  Google Scholar 

  50. Reutlinger M, Koch CP, Reker D et al (2013) Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for ‘orphan’ molecules. Mol Inf 32:133–138

    Article  CAS  Google Scholar 

  51. Schueler FWP (1960) Chemobiodynamics and drug design. McGraw-Hill Book Company, Inc., New York

    Google Scholar 

  52. Wermuth CG, Ganellin CR, Lindberg P et al (1998) Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl Chem 70:1129

    Article  CAS  Google Scholar 

  53. Varnek A, Fourches D, Horvath D et al (2008) ISIDA-platform for virtual screening based on fragment and pharmacophoric descriptors. Curr Comput Aided Drug Des 4:191

    Article  CAS  Google Scholar 

  54. Good AC, Cho S-J, Mason JS (2004) Descriptors you can count on? Normalized and filtered pharmacophore descriptors for virtual screening. J Comput Aided Mol Des 18:523–527

    Article  CAS  PubMed  Google Scholar 

  55. Pickett SD, Luttmann C, Guerin V et al (1998) DIVSEL and COMPLIB - strategies for the design and comparison of combinatorial libraries using pharmacophoric descriptors. J Chem Inf Comput Sci 38:144–150

    Article  CAS  PubMed  Google Scholar 

  56. Nettles JH, Jenkins JL, Williams C et al (2007) Flexible 3D pharmacophores as descriptors of dynamic biological space. Graham Richards 67th Birthd Honour Issue 26:622–633

    CAS  Google Scholar 

  57. Renner S, Hechenberger M, Noeske T et al (2007) Searching for drug scaffolds with 3D pharmacophores and neural network ensembles. Angew Chem Int Ed 46:5336–5339

    Article  CAS  Google Scholar 

  58. Tanrikulu Y, Nietert M, Scheffer U et al (2007) Scaffold hopping by “fuzzy” pharmacophores and its application to RNA targets. Chembiochem 8:1932–1936

    Article  CAS  PubMed  Google Scholar 

  59. Stiefl N, Watson IA, Baumann K et al (2006) ErG: 2D pharmacophore descriptions for scaffold hopping. J Chem Inf Model 46:208–220

    Article  CAS  PubMed  Google Scholar 

  60. Jenkins JL, Glick M, Davies JW (2004) A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes. J Med Chem 47:6144–6159

    Article  CAS  PubMed  Google Scholar 

  61. Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25:64–73

    Article  CAS  Google Scholar 

  62. Rodrigues T, Schneider G (2014) Flashback forward: reaction-driven de novo design of bioactive compounds. Synlett 25:170–178

    CAS  Google Scholar 

  63. Schneider G (2013) De novo design – hop(p)ing against hope. Drug Discov Today Technol 10:e453–e460

    Article  PubMed  Google Scholar 

  64. Awale M, Reymond J-L (2014) Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. J Chem Inf Model 54:1892–1907

    Article  CAS  PubMed  Google Scholar 

  65. Grant JA, Gallardo MA, Pickup BT (1996) A fast method of molecular shape comparison: a simple application of a Gaussian description of molecular shape. J Comput Chem 17:1653–1666

    Article  CAS  Google Scholar 

  66. Rush TS, Grant JA, Mosyak L et al (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein−protein interaction. J Med Chem 48:1489–1495

    Article  CAS  PubMed  Google Scholar 

  67. Liu X, Jiang H, Li H (2011) SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. J Chem Inf Model 51:2372–2385

    Article  CAS  PubMed  Google Scholar 

  68. Ge H, Wang Y, Zhao W et al (2014) Scaffold hopping of potential anti-tumor agents by WEGA: a shape-based approach. Med Chem Comm 5:737–741

    Article  CAS  Google Scholar 

  69. Schuffenhauer A (2012) Computational methods for scaffold hopping. WIREs Comput Mol Sci 2:842–867

    Article  CAS  Google Scholar 

  70. Grisoni F, Merk D, Consonni V et al (2018) Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity. Commun Chem 1:44

    Article  CAS  Google Scholar 

  71. Grisoni F, Merk D, Byrne R et al (2018) Scaffold-hopping from synthetic drugs by holistic molecular representation. Sci Rep 8:16469

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Todeschini R, Ballabio D, Consonni V et al (2013) Locally centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Anal Chim Acta 787:1–9

    Article  CAS  PubMed  Google Scholar 

  73. Grisoni F, Merk D, Friedrich L et al (2019) Design of natural-product-inspired multitarget ligands by machine learning. ChemMedChem 14:1129–1134

    Article  CAS  PubMed  Google Scholar 

  74. Merk D, Grisoni F, Friedrich L et al (2018) Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators. Commun Chem 1:68

    Article  Google Scholar 

  75. Merk D, Friedrich L, Grisoni F et al (2018) De novo design of bioactive small molecules by artificial intelligence. Mol Inf 37

    Google Scholar 

  76. Merk D, Grisoni F, Friedrich L et al (2018) Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics. J Med Chem 61:5442–5447

    Article  CAS  PubMed  Google Scholar 

  77. Cao D-S, Liang Y-Z, Yan J et al (2013) PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. J Chem Inf Model 53:3086–3096

    Article  CAS  PubMed  Google Scholar 

  78. Nugmanov RI, Mukhametgaleev RN, Akhmetshin T et al (2019) CGRtools: python library for molecule, reaction, and condensed graph of reaction processing. J Chem Inf Model 59:2516–2521

    Article  CAS  PubMed  Google Scholar 

  79. Cao D-S, Xu Q-S, Hu Q-N et al (2013) ChemoPy: freely available python package for computational biology and chemoinformatics. Bioinformatics 29:1092–1094

    Article  CAS  PubMed  Google Scholar 

  80. Tangadpalliwar SR, Vishwakarma S, Nimbalkar R et al (2019) ChemSuite: a package for chemoinformatics calculations and machine learning. Chem Biol Drug Des 93:960–964

    Article  CAS  PubMed  Google Scholar 

  81. Müller AT, Gabernet G, Hiss JA et al (2017) modlAMP: Python for antimicrobial peptides. Bioinformatics 33:2753–2755

    Article  PubMed  CAS  Google Scholar 

  82. Kluyver T, Ragan-Kelley B, Pérez F et al (2016) Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B (eds) Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press, pp 87–90

    Google Scholar 

  83. Yan Y, Yan J (2018) Hands-on data science with Anaconda: utilize the right mix of tools to create high-performance data science applications. Packt Publishing Ltd

    Google Scholar 

  84. Loeliger J, McCullough M (2012) Version control with Git: powerful tools and techniques for collaborative software development. O’Reilly Media, Inc

    Google Scholar 

  85. Dabbish L, Stuart C, Tsay J et al (2012) Social coding in GitHub: transparency and collaboration in an open software repository. In: Proceedings of the ACM 2012 conference on computer supported cooperative work. Association for Computing Machinery, New York, pp 1277–1286

    Chapter  Google Scholar 

  86. Koehn FE, Carter GT (2005) The evolving role of natural products in drug discovery. Nat Rev Drug Discov 4:206–220

    Article  CAS  PubMed  Google Scholar 

  87. Patridge E, Gareiss P, Kinch MS et al (2016) An analysis of FDA-approved drugs: natural products and their derivatives. Drug Discov Today 21:204–207

    Article  CAS  PubMed  Google Scholar 

  88. Lee M-L, Schneider G (2001) Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries. J Comb Chem 3:284–289

    Article  CAS  PubMed  Google Scholar 

  89. Brown DG, Lister T, May-Dracka TL (2014) New natural products as new leads for antibacterial drug discovery. Bioorg Med Chem Lett 24:413–418

    Article  CAS  PubMed  Google Scholar 

  90. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminformatics 1:8

    Article  CAS  Google Scholar 

  91. Atanasov AG, Waltenberger B, Pferschy-Wenzig E-M et al (2015) Discovery and resupply of pharmacologically active plant-derived natural products: a review. Biotechnol Adv 33:1582–1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Grabowski K, Proschak E, Baringhaus K-H et al (2008) Bioisosteric replacement of molecular scaffolds: from natural products to synthetic compounds. Nat Prod Commun 3:1934578X0800300821

    Google Scholar 

  93. Ongini E, Monopoli A, Cacciari B et al (2001) Selective adenosine A2A receptor antagonists. Il Farm 56:87–90

    Article  CAS  Google Scholar 

  94. Lamberth C (2018) Agrochemical lead optimization by scaffold hopping. Pest Manag Sci 74:282–292

    Article  CAS  PubMed  Google Scholar 

  95. Wiley RA, Rich DH (1993) Peptidomimetics derived from natural products. Med Res Rev 13:327–384

    Article  CAS  PubMed  Google Scholar 

  96. Akbulut Y, Gaunt HJ, Muraki K et al (2015) (−)-Englerin A is a potent and selective activator of TRPC4 and TRPC5 calcium channels. Angew Chem Int Ed 54:3787–3791

    Article  CAS  Google Scholar 

  97. Ratnayake R, Covell D, Ransom TT et al (2009) Englerin A, a selective inhibitor of renal cancer cell growth, from Phyllanthus engleri. Org Lett 11:57–60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Friedrich L, Byrne R, Treder A et al (2020) Shape similarity by fractal dimensionality: an application in de novo design of (−)-Englerin A mimetics, accepted. ChemMedChem 15:566

    Article  CAS  PubMed  Google Scholar 

  99. Sterling T, Irwin JJ (2015) ZINC 15 – ligand discovery for everyone. J Chem Inf Model 55:2324–2337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39:2887–2893

    Article  CAS  PubMed  Google Scholar 

  101. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  102. Dalby A, Nourse JG, Hounshell WD et al (1992) Description of several chemical structure file formats used by computer programs developed at molecular design limited. J Chem Inf Comput Sci 32:244–255

    Article  CAS  Google Scholar 

  103. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519

    Article  CAS  Google Scholar 

  104. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228

    Article  CAS  Google Scholar 

  105. Aradi B, Hourahine B, Frauenheim T (2007) DFTB+, a sparse matrix-based implementation of the DFTB method. J Phys Chem A 111:5678–5684

    Article  CAS  PubMed  Google Scholar 

  106. Blaschke T, Olivecrona M, Engkvist O et al (2018) Application of generative autoencoder in de novo molecular design. Mol Inf 37:1700123

    Article  CAS  Google Scholar 

  107. Button A, Merk D, Hiss JA et al (2019) Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nat Mach Intell 1:307

    Article  Google Scholar 

  108. Hartenfeller M, Zettl H, Walter M et al (2012) DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8:e1002380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Lloyd DG, Buenemann CL, Todorov NP et al (2004) Scaffold hopping in de novo design. Ligand generation in the absence of receptor information. J Med Chem 47:493–496

    Article  CAS  PubMed  Google Scholar 

  110. Truchon J-F, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47:488–508

    Article  CAS  PubMed  Google Scholar 

  111. Zhu T, Cao S, Su P-C et al (2013) Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem 56:6560–6572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Hu Y, Stumpfe D, Bajorath J (2011) Lessons learned from molecular scaffold analysis. J Chem Inf Model 51:1742–1753

    Article  CAS  PubMed  Google Scholar 

  113. Xu Y, Johnson M (2001) Algorithm for naming molecular equivalence classes represented by labeled pseudographs. J Chem Inf Comput Sci 41:181–185

    Article  CAS  PubMed  Google Scholar 

  114. Sauer WHB, Schwarz MK (2003) Size doesn’t matter: scaffold diversity, shape diversity and biological activity of combinatorial libraries. Chim Int J Chem 57:276–283

    Article  CAS  Google Scholar 

  115. Medina-Franco JL, Martínez-Mayorga K, Bender A et al (2009) Scaffold diversity analysis of compound data sets using an entropy-based measure. QSAR Comb Sci 28:1551–1560

    Article  CAS  Google Scholar 

  116. O’Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminformatics 8:36

    Article  CAS  Google Scholar 

  117. Pyzer-Knapp O, EN, Simm G, Guzik AA (2016) A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials. Mater Horiz 3:226–233

    Article  CAS  Google Scholar 

  118. Besnard J, Ruda GF, Setola V et al (2012) Automated design of ligands to polypharmacological profiles. Nature 492:215–220

    Article  CAS  PubMed  Google Scholar 

  119. Hert J, Willett P, Wilton DJ et al (2004) Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Comput Sci 44:1177–1185

    Article  CAS  PubMed  Google Scholar 

  120. Ripphausen P, Nisius B, Peltason L et al (2010) Quo Vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53:8461–8467

    Article  CAS  PubMed  Google Scholar 

  121. Chen B, Mueller C, Willett P (2010) Combination rules for group fusion in similarity-based virtual screening. Mol Inf 29:533–541

    Article  CAS  Google Scholar 

  122. Whittle M, Gillet VJ, Willett P et al (2006) Analysis of data fusion methods in virtual screening: similarity and group fusion. J Chem Inf Model 46:2206–2219

    Article  CAS  PubMed  Google Scholar 

  123. Willett P (2006) Enhancing the effectiveness of ligand-based virtual screening using data fusion. QSAR Comb Sci 25:1143–1152

    Article  CAS  Google Scholar 

  124. Rybinska A, Sosnowska A, Barycki M et al (2016) Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids. J Comput Aided Mol Des 30:165–176

    Article  CAS  PubMed  Google Scholar 

  125. Riniker S, Landrum GA (2015) Better informed distance geometry: using what we know to improve conformation generation. J Chem Inf Model 55:2562–2574

    Article  CAS  PubMed  Google Scholar 

  126. Nicklaus MC, Wang S, Driscoll JS et al (1995) Conformational changes of small molecules binding to proteins. Bioorg Med Chem 3:411–428

    Article  CAS  PubMed  Google Scholar 

  127. Tomich de Paula da Silva CH, Taft CA (2017) 3D descriptors calculation and conformational search to investigate potential bioactive conformations, with application in 3D-QSAR and virtual screening in drug design. J Biomol Struct Dyn 35:2966–2974

    Google Scholar 

  128. Perola E, Charifson PS (2004) Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem 47:2499–2510

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francesca Grisoni .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Grisoni, F., Schneider, G. (2021). Molecular Scaffold Hopping via Holistic Molecular Representation. In: Ballante, F. (eds) Protein-Ligand Interactions and Drug Design. Methods in Molecular Biology, vol 2266. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1209-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1209-5_2

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1208-8

  • Online ISBN: 978-1-0716-1209-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics