Skip to main content
SpringerLink
Log in

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

  • Full-Length Paper
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

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

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

ACEF:

Activity cliff enrichment factor

COX:

Cyclooxygenase

DAT:

Dopamine transporter

ECFP:

Extended connectivity fingerprint

EstateIndices:

Electrotopological state indices

MACCS:

Molecular ACCess System

MATs:

Monoamine transporters

MEQI:

Molecular Equivalence Indices

MEQNUM:

Molecular equivalence number

NAC/CF:

Number of activity cliffs / chemotype frequency

NET:

Norepinephrine transporter

NSGs:

Network-like similarity graphs

PPAR:

Peroxisome proliferator-activated receptor

QSAR:

Quantitative structure–activity relationships

ROCS:

Rapid overlay of chemical structures

SALI:

Structure–activity landscape index

SARI:

SAR index

SAS:

Structure–activity similarity

SERT:

Serotonin transporter

SAR:

Structure–activity relationships

TopAtomPairs:

Topological atom pairs

TopPh4AtomPairs:

Topological pharmacophore atom pairs

TopAtomTorsions:

Topological atom torsions

TopAtomTriplets:

Topological atom triplets

TopPh4AtomTriplets:

Topological pharmacophore atom triplets

References

  1. Maggiora GM (2006) On outliers and activity cliffs: why QSAR often disappoints. J Chem Inf Model 46:1535. doi:10.1021/ci060117s

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  3. Stumpfe D, Bajorath J (2012) Exploring activity cliffs in medicinal chemistry. J Med Chem 55:2932–2942. doi:10.1021/jm201706b

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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. C

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  17. Schneider C, Pozzi A (2011) Cyclooxygenases and lipoxygenases in cancer. Cancer Metastasis Rev 30:277–294. doi:10.1007/s10555-011-9310-3

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Moller DE (2001) New drug targets for type 2 diabetes and the metabolic syndrome. Nature 414:821–827. doi:10.1038/414821a

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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. 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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  30. Xu J (2002) A new approach to finding natural chemical structure classes. J Med Chem 45:5311–5320. doi:10.1021/jm010520k

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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 16:331–341

    PubMed  Google Scholar 

  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. C

  34. ROCS 3.1.0. OpenEye Scientific Software, Santa Fe. http://www.eyesopen.com

  35. Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaudoise Sci Nat 37:547–549

    Google Scholar 

  36. Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996. doi:10.1021/ci9800211

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  39. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. doi:10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  43. Renner S, Fechner U, Schneider G (2006) In pharmacophores and pharmacophore searches, vol 32. Wiley-VCH, Weinheim

    Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  46. Vconf v2.0. VeraChem LLC, Germantown 2004. http://www.verachem.com

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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. 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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

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.

Author information

Author notes

  1. Oscar Méndez-Lucio

    Present address: Unilever Centre for Molecular Science Informatics Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK

Authors and Affiliations

  1. División de Ciencias Biológicas y de la Salud, Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana Unidad Xochimilco (UAM-X), 04960, Mexico, DF, Mexico

    Jaime Pérez-Villanueva & Olivia Soria-Arteche

  2. Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico, DF, Mexico

    Oscar Méndez-Lucio & José L. Medina-Franco

Authors
  1. Jaime Pérez-Villanueva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Méndez-Lucio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olivia Soria-Arteche
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José L. Medina-Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaime Pérez-Villanueva.

Additional information

This paper is dedicated to the Memory of Dra. Maria Concepción Lozada García.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1144 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pérez-Villanueva, J., Méndez-Lucio, O., Soria-Arteche, O. et al. Activity cliffs and activity cliff generators based on chemotype-related activity landscapes. Mol Divers 19, 1021–1035 (2015). https://doi.org/10.1007/s11030-015-9609-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-015-9609-z

Keywords

Search

Navigation