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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

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

Keywords

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

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

Notes

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.

Supplementary material

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

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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

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