Evolution of the activity cliff concept for structure-activity relationship analysis and drug discovery.

Abstract

Future Medicinal ChemistryVol. 6, No. 14 CommentaryEvolution of the activity cliff concept for structure–activity relationship analysis and drug discoveryJürgen BajorathJürgen BajorathSearch for more papers by this authorPublished Online:4 Nov 2014https://doi.org/10.4155/fmc.14.94AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail View articleKeywords: active compoundsactivity cliffscompound data mininglead optimizationmolecular similaritySAR analysisReferences1 Stumpfe D, Bajorath J. Exploring activity cliffs in medicinal chemistry. J. Med. Chem. 55(7), 2932–2942 (2012).Crossref, Medline, CAS, Google Scholar2 Stumpfe D, Hu Y, Dimova D, Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J. Med. Chem. 57(1), 18–28 (2014).Crossref, Medline, CAS, Google Scholar3 Hu Y, Bajorath J. 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(7), 1806–1811 (2012).Crossref, Medline, CAS, Google Scholar4 Griffen E, Leach AG, Robb GR, Warner DJ. Matched molecular pairs as a medicinal chemistry tool. J. Med. Chem. 54(22), 7739–7750 (2011).Crossref, Medline, CAS, Google Scholar5 Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath, J. MMP-cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J. Chem. Inf. Model 52(5), 1138–1145 (2012).Crossref, Medline, CAS, Google Scholar6 Hu Y, Stumpfe D, Bajorath J. Advancing the activity cliff concept [v1; ref status: indexed, http://f1000r.es/1wf] (2013). http://f1000research.com/articles/2-199/v1 Crossref, Google Scholar7 Gaulton A, Bellis LJ, Bento AP et al. ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res. 40(Database issue), D1100–D1107 (2012).Crossref, Medline, CAS, Google Scholar8 Stumpfe D, Bajorath J. Frequency of occurrence and potency range distribution of activity cliffs in bioactive compounds. J. Chem. Inf. Model. 52(9), 2348–2353 (2012).Crossref, Medline, CAS, Google Scholar9 Stumpfe D, de la Vega de León A, Dimova D, Bajorath, J. Advancing the activity cliff concept, part II. [v1; ref status: indexed, http://f1000r.es/34p] (2014). http://f1000research.com/articles/3-75/v1 Crossref, Google Scholar10 Stumpfe D, Dimova D, Heikamp K, Bajorath J. Compound pathway model to capture SAR progression: comparison of activity cliff-dependent and -independent pathways. J. Chem. Inf. Model 53(5), 1067–1072 (2013).Crossref, Medline, CAS, Google Scholar11 Dimova D, Heikamp K, Stumpfe D Bajorath J. Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets. J. Med. Chem. 56(8), 3339–3345 (2013).Crossref, Medline, CAS, Google Scholar12 Stumpfe D, Dimova D, Bajorath J. Composition and topology of activity cliff clusters formed by bioactive compounds. J. Chem. Inf. Model 54(2), 451–561 (2014).Crossref, Medline, CAS, Google ScholarFiguresReferencesRelatedDetailsCited ByDrug target inference by mining transcriptional data using a novel graph convolutional network framework22 October 2021 | Protein & Cell, Vol. 13, No. 4Navigating into the Chemical Space of Monoamine Oxidase Inhibitors by Artificial Intelligence and Cheminformatics Approach1 September 2021 | ACS Omega, Vol. 6, No. 36Systematic Exploration of Activity Cliffs Containing Privileged Substructures24 January 2020 | Molecular Pharmaceutics, Vol. 17, No. 3Computational method for the identification of third generation activity cliffsMethodsX, Vol. 7Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutionsBioorganic & Medicinal Chemistry, Vol. 27, No. 16Second-generation activity cliffs identified on the basis of target set-dependent potency difference criteriaHuabin Hu, Dagmar Stumpfe & Jürgen Bajorath19 March 2019 | Future Medicinal Chemistry, Vol. 11, No. 5Systematic identification of target set-dependent activity cliffsHuabin Hu, Dagmar Stumpfe & Jürgen Bajorath18 January 2019 | Future Science OA, Vol. 5, No. 27-Substituted umbelliferone derivatives as androgen receptor antagonists for the potential treatment of prostate and breast cancerBioorganic & Medicinal Chemistry Letters, Vol. 26, No. 8Monitoring global growth of activity cliff information over time and assessing activity cliff frequencies and distributionsDagmar Stumpfe & Jürgen Bajorath27 August 2015 | Future Medicinal Chemistry, Vol. 7, No. 12 Vol. 6, No. 14 Follow us on social media for the latest updates Metrics Downloaded 120 times History Published online 4 November 2014 Published in print September 2014 Information© Future Science LtdKeywordsactive compoundsactivity cliffscompound data mininglead optimizationmolecular similaritySAR analysisFinancial & competing interests disclosureThe author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.No writing assistance was utilized in the production of this manuscript.PDF download