Molecular Diversity

, Volume 19, Issue 4, pp 1021–1035 | Cite as

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes

  • Jaime Pérez-VillanuevaEmail author
  • Oscar Méndez-Lucio
  • Olivia Soria-Arteche
  • José L. Medina-Franco
Full-Length Paper


Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure–activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.

Graphic abstract


Activity cliff generators Activity cliff enrichment factor Chemotype SAS maps  Structure–activity relationships 



Activity cliff enrichment factor




Dopamine transporter


Extended connectivity fingerprint


Electrotopological state indices


Molecular ACCess System


Monoamine transporters


Molecular Equivalence Indices


Molecular equivalence number


Number of activity cliffs / chemotype frequency


Norepinephrine transporter


Network-like similarity graphs


Peroxisome proliferator-activated receptor


Quantitative structure–activity relationships


Rapid overlay of chemical structures


Structure–activity landscape index


SAR index


Structure–activity similarity


Serotonin transporter


Structure–activity relationships


Topological atom pairs


Topological pharmacophore atom pairs


Topological atom torsions


Topological atom triplets


Topological pharmacophore atom triplets



The authors would like to express their sincere gratitude to the BindingDB team for providing the studied structure and activity data; to Dr. Mark Johnson for providing the program MEQI; to MayaChemTools for providing the scripts for fingerprint calculations; to VeraChem LLC for providing VConf; to OpenEye Scientific Software, Inc., for providing ROCS (UAM); and to Tableau Software for providing Tableau Public. O. M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. JL. M-F thanks the National Autonomous University of Mexico (UNAM), grant PAIP 5000-9163, for funding.

Supplementary material

11030_2015_9609_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (pdf 1144 KB)


  1. 1.
    Maggiora GM (2006) On outliers and activity cliffs: why QSAR often disappoints. J Chem Inf Model 46:1535. doi: 10.1021/ci060117s CrossRefPubMedGoogle Scholar
  2. 2.
    Cruz-Monteagudo M, Medina-Franco JL, Pérez-Castillo Y, Nicolotti O, Cordeiro MNDS, Borges F (2014) Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discov Today 19:1069–1080. doi: 10.1016/j.drudis.2014.02.003 CrossRefPubMedGoogle Scholar
  3. 3.
    Stumpfe D, Bajorath J (2012) Exploring activity cliffs in medicinal chemistry. J Med Chem 55:2932–2942. doi: 10.1021/jm201706b CrossRefPubMedGoogle Scholar
  4. 4.
    Pérez-Villanueva J, Medina-Franco JL, Caulfield TR, Hernández-Campos A, Hernández-Luis F, Yépez-Mulia L, Castillo R (2011) Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of some benzimidazole derivatives with trichomonicidal activity. Eur J Med Chem 46:3499–3508. doi: 10.1016/j.ejmech.2011.05.016 CrossRefPubMedGoogle Scholar
  5. 5.
    Hernández-Vázquez E, Méndez-Lucio O, Hernández-Luis F (2013) Activity landscape analysis, CoMFA and CoMSIA studies of pyrazole CB1 antagonists. Med Chem Res 22:4133–4145. doi: 10.1007/s00044-012-0418-y CrossRefGoogle Scholar
  6. 6.
    Iyer P, Wawer M, Bajorath J (2011) Comparison of two- and three-dimensional activity landscape representations for different compound data sets. Med Chem Commun 2:113–118. doi: 10.1039/c0md00188k CrossRefGoogle Scholar
  7. 7.
    Shanmugasundaram V, Maggiora GM (2001) Characterizing property and activity landscapes using an information-theoretic approach. Cinf-032. In: 222nd ACS national meeting, Chicago. American Chemical Society, Washington, D. CGoogle Scholar
  8. 8.
    Wawer M, Peltason L, Weskamp N, Teckentrup A, Bajorath J (2008) Structure–activity relationship anatomy by network-like similarity graphs and local structure–activity relationship indices. J Med Chem 51:6075–6084. doi: 10.1021/jm800867g CrossRefPubMedGoogle Scholar
  9. 9.
    Guha R, Van Drie JH (2008) Structure–activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 48:646–658. doi: 10.1021/ci7004093 CrossRefPubMedGoogle Scholar
  10. 10.
    Peltason L, Bajorath J (2007) SAR index: quantifying the nature of structure–activity relationships. J Med Chem 50:5571–5578. doi: 10.1021/jm0705713 CrossRefPubMedGoogle Scholar
  11. 11.
    Bajorath J, Peltason L, Wawer M, Guha R, Lajiness MS, Van Drie JH (2009) Navigating structure–activity landscapes. Drug Discov Today 14:698–705. doi: 10.1016/j.drudis.2009.04.003 CrossRefPubMedGoogle Scholar
  12. 12.
    Kayastha S, Dimova D, Iyer P, Vogt M, Bajorath J (2014) Large-scale assessment of activity landscape feature probabilities of bioactive compounds. J Chem Inf Model 54:442–450. doi: 10.1021/ci400677b CrossRefPubMedGoogle Scholar
  13. 13.
    Méndez-Lucio O, Pérez-Villanueva J, Castillo R, Medina-Franco JL (2012) Identifying activity cliff generators of PPAR ligands using SAS maps. Mol Inf 31:837–846. doi: 10.1002/minf.201200078 CrossRefGoogle Scholar
  14. 14.
    Hu Y, Bajorath J (2012) Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J Chem Inf Model 52:1806–1811. doi: 10.1021/ci300274c CrossRefPubMedGoogle Scholar
  15. 15.
    Jayanthi LD, Ramamoorthy S (2005) Regulation of monoamine transporters: influence of psychostimulants and therapeutic antidepressants. AAPS J 7:E728–E738. doi: 10.1208/aapsj070373 PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Torres GE, Gainetdinov RR, Caron MG (2003) Plasma membrane monoamine transporters: structure, regulation and function. Nat Rev Neurosci 4:13–25. doi: 10.1038/nrn1008 CrossRefPubMedGoogle Scholar
  17. 17.
    Schneider C, Pozzi A (2011) Cyclooxygenases and lipoxygenases in cancer. Cancer Metastasis Rev 30:277–294. doi: 10.1007/s10555-011-9310-3 PubMedCentralCrossRefPubMedGoogle Scholar
  18. 18.
    Kirane A, Toombs JE, Ostapoff K, Carbon JG, Zaknoen S, Braunfeld J, Schwarz RE, Burrows FJ, Brekken RA (2012) Apricoxib, a novel inhibitor of COX-2, markedly improves standard therapy response in molecularly defined models of pancreatic cancer. Clin Cancer Res 18:5031–5042. doi: 10.1158/1078-0432.CCR-12-0453 PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Moller DE (2001) New drug targets for type 2 diabetes and the metabolic syndrome. Nature 414:821–827. doi: 10.1038/414821a CrossRefPubMedGoogle Scholar
  20. 20.
    Balakumar P, Rose M, Ganti SS, Krishan P, Singh M (2007) PPAR dual agonists: are they opening pandora’s box? Pharmacol Res 56:91–98. doi: 10.1016/j.phrs.2007.03.002 CrossRefPubMedGoogle Scholar
  21. 21.
    Méndez-Lucio O, Pérez-Villanueva J, Castillo R, Medina-Franco JL (2012) Activity landscape modeling of PPAR ligands with dual-activity difference maps. Bioorg Med Chem 20:3523–3532. doi: 10.1016/j.bmc.2012.04.005 CrossRefPubMedGoogle Scholar
  22. 22.
    Dimova D, Wawer M, Wassermann AM, Bajorath J (2011) Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. J Chem Inf Model 51:258–266. doi: 10.1021/ci100477m CrossRefPubMedGoogle Scholar
  23. 23.
    Pérez-Villanueva J, Medina-Franco JL, Méndez-Lucio O, Yoo J, Soria-Arteche O, Izquierdo T, Lozada MC, Castillo R (2012) CASE plots for the chemotype-based activity and selectivity analysis: a CASE study of cyclooxygenase inhibitors. Chem Biol Drug Des 80:752–762. doi: 10.1111/cbdd.12019 CrossRefPubMedGoogle Scholar
  24. 24.
    Medina-Franco JL, Yongye AB, Pérez-Villanueva J, Houghten RA, Martínez-Mayorga K (2011) Multitarget structure–activity relationships characterized by activity-difference maps and consensus similarity measure. J Chem Inf Model 51:2427–2439. doi: 10.1021/ci200281v CrossRefPubMedGoogle Scholar
  25. 25.
    Chen X, Lin Y, Gilson MK (2001) The binding database: overview and user’s guide. Biopolymers 61:127–141. doi: 10.1002/1097-0282(2002)61:2lt127:AID-BIP10076>3.0.CO;2-N
  26. 26.
    Chen X, Lin Y, Liu M, Gilson MK (2002) The binding database: data management and interface design. Bioinformatics 18:130–139. doi: 10.1093/bioinformatics/18.1.130 CrossRefPubMedGoogle Scholar
  27. 27.
    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–D201. doi: 10.1093/nar/gkl999 PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Xu YJ, Johnson M (2001) Algorithm for naming molecular equivalence classes represented by labeled pseudographs. J Chem Inf Comput Sci 41:181–185. doi: 10.1021/ci0003911 CrossRefPubMedGoogle Scholar
  29. 29.
    Xu YJ, Johnson M (2002) Using molecular equivalence numbers to visually explore structural features that distinguish chemical libraries. J Chem Inf Comput Sci 42:912–926. doi: 10.1021/ci025535l CrossRefPubMedGoogle Scholar
  30. 30.
    Xu J (2002) A new approach to finding natural chemical structure classes. J Med Chem 45:5311–5320. doi: 10.1021/jm010520k CrossRefPubMedGoogle Scholar
  31. 31.
    Xu J, Gu Q, Liu H, Zhou J, Bu X, Huang Z, Lu G, Li D, Wei D, Wang L, Gu L (2013) Chemomics and drug innovation. Sci China Chem 56:71–85. doi: 10.1007/s11426-012-4761-0 CrossRefGoogle Scholar
  32. 32.
    Gu Q, Yan X, Xu J (2013) Drug discovery inspired by mother nature: seeking natural biochemotypes and the natural assembly rules of the biochemome. J Pharm Pharm Sci 16:331–341PubMedGoogle Scholar
  33. 33.
    Sud M (2012) MayaChemTools: an open source package for computational discovery. Comp-306, In 243nd ACS National Meeting, San Diego. American Chemical Society, Washington, D. CGoogle Scholar
  34. 34.
    ROCS 3.1.0. OpenEye Scientific Software, Santa Fe.
  35. 35.
    Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaudoise Sci Nat 37:547–549Google Scholar
  36. 36.
    Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996. doi: 10.1021/ci9800211 CrossRefGoogle Scholar
  37. 37.
    Filimonov D, Poroikov V, Borodina Y, Gloriozova T (1999) Chemical similarity assessment through multilevel neighborhoods of atoms: definition and comparison with the other descriptors. J Chem Inf Comput Sci 39:666–670. doi: 10.1021/ci980335o CrossRefGoogle Scholar
  38. 38.
    Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information. J Chem Inf Comput Sci 35:1039–1045. doi: 10.1021/ci00028a014 CrossRefGoogle Scholar
  39. 39.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. doi: 10.1021/ci100050t CrossRefPubMedGoogle Scholar
  40. 40.
    Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42:1273–1280. doi: 10.1021/ci010132r CrossRefPubMedGoogle Scholar
  41. 41.
    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. doi: 10.1021/ci00046a002 CrossRefGoogle Scholar
  42. 42.
    Nilakantan R, Bauman N, Dixon JS, Venkataraghavan R (1987) Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors. J Chem Inf Comput Sci 27:82–85. doi: 10.1021/ci00054a008 CrossRefGoogle Scholar
  43. 43.
    Renner S, Fechner U, Schneider G (2006) In pharmacophores and pharmacophore searches, vol 32. Wiley-VCH, WeinheimGoogle Scholar
  44. 44.
    Bonachéra F, Parent B, Barbosa F, Froloff N, Horvath D (2006) Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted molecular similarity scoring schemes. J Chem Inf Model 46:2457–2477. doi: 10.1021/ci6002416 CrossRefPubMedGoogle Scholar
  45. 45.
    Chang CE, Gilson MK (2003) Tork: conformational analysis method for molecules and complexes. J Comput Chem 24:1987–1998. doi: 10.1002/jcc.10325 CrossRefPubMedGoogle Scholar
  46. 46.
    Vconf v2.0. VeraChem LLC, Germantown 2004.
  47. 47.
    Yongye AB, Byler K, Santos R, Martínez-Mayorga K, Maggiora GM, Medina-Franco JL (2011) Consensus models of activity landscapes with multiple chemical, conformer, and property representations. J Chem Inf Model 51:1259–1270. doi: 10.1021/ci200081k CrossRefPubMedGoogle Scholar
  48. 48.
    Rush TS III, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3D scaffold hopping method and its application to a bacterial protein–protein interaction. J Med Chem 48:1489–1495. doi: 10.1021/jm040163o CrossRefPubMedGoogle Scholar
  49. 49.
    Sykes MJ, Sorich MJ, Miners JO (2006) Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes. J Chem Inf Model 46:2661–2673. doi: 10.1021/ci600221h CrossRefPubMedGoogle Scholar
  50. 50.
    Medina-Franco JL, Martínez-Mayorga K, Bender A, Marín RM, Giulianotti MA, Pinilla C, Houghtent RA (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49:477–491. doi: 10.1021/ci800379q CrossRefPubMedGoogle Scholar
  51. 51.
    Chen B, Mueller C, Willett P (2010) Combination rules for group fusion in similarity-based virtual screening. Mol Inf 29:533–541. doi: 10.1002/minf.201000050 CrossRefGoogle Scholar
  52. 52.
    Medina-Franco JL (2012) Scanning structure–activity relationships with structure–activity similarity and related maps: from consensus activity cliffs to selectivity switches. J Chem Inf Model 52:2485–2493. doi: 10.1021/ci300362x CrossRefPubMedGoogle Scholar
  53. 53.
    Pérez-Villanueva J, Santos R, Hernández-Campos A, Giulianotti MA, Castillo R, Medina-Franco JL (2011) Structure–activity relationships of benzimidazole derivatives as antiparasitic agents: dual activity-difference (DAD) maps. Med Chem Commun 2:44–49. doi: 10.1039/c0md00159g
  54. 54.
    Medina-Franco JL, Petit J, Maggiora GM (2006) Hierarchical strategy for identifying active chemotype classes in compound databases. Chem Biol Drug Des 67:395–408. doi: 10.1111/j.1747-0285.2006.00397.x CrossRefPubMedGoogle Scholar
  55. 55.
    Pérez-Villanueva J, Santos R, Hernández-Campos A, Giulianotti MA, Castillo R, Medina-Franco JL (2010) Towards a systematic characterization of the antiprotozoal activity landscape of benzimidazole derivatives. Bioorg Med Chem 18:7380–7391. doi: 10.1016/j.bmc.2010.09.019 CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.División de Ciencias Biológicas y de la Salud, Departamento de Sistemas BiológicosUniversidad Autónoma Metropolitana Unidad Xochimilco (UAM-X)MexicoMexico
  2. 2.Departamento de Farmacia, Facultad de QuímicaUniversidad Nacional Autónoma de México (UNAM)MexicoMexico
  3. 3.Unilever Centre for Molecular Science Informatics Department of ChemistryUniversity of CambridgeCambridgeUK

Personalised recommendations