Medina-Franco JL, Martinez-Mayorga K

Language: Spanish
References: 21
Page: 14-23
PDF size: 667.36 Kb.

ABSTRACT

Activity cliffs are defined as compounds with high structure similarity but large potency difference. Identification of activity cliffs have a significant impact in lead optimization in medicinal chemistry, and computational applications such as the development of predictive models and the selection of queries for similarity searching. Therefore, the identification of compounds highly associated with activity cliffs in a given data set i.e., ‘activity cliff generators’, is of major relevance. Herein, we report the identification of activity cliffs and structure-activity relationships of a set of 289 synthetic compounds tested in a G protein-coupled receptor kinase, GRK. To account for information of multiple structure representations we used mean Structure-Activity Landscape Index (SALI). Structural fragments responsible for the activity are discussed.

REFERENCES

1. Canvas Canvas, version 1.5; Schrödinger, LLC, New York, NY, 2012., Canvas, version 1.5; Schrödinger, LLC, New York, NY, 2012.

2. Cruz-Monteagudo, M., Medina-Franco, J.L., 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

3. Dimova, D., Stumpfe, D. & Bajorath, J.(2014). Method for the Evaluation of Structure–Activity Relationship Information Associated with Coordinated Activity Cliffs. J. Med. Chem., 57, 6553-6563. DOI: 10.1021/jm500577n

4. Guha, R. (2012). You have full text access to this contentExploring structure–activity data using the landscape paradigm. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2, 829-841. DOI: 10.1002/ wcms.1087

5. Guha, R. & VanDrie, J.H. (2008). Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs. J. Chem. Inf. Model., 48, 646-658. DOI: 10.1021/ci7004093

6. Guha, R. & Van Drie, J.H. (2008). Assessing How Well a Modeling Protocol Captures a Structure−Activity Landscape. J. Chem. Inf. Model., 48, 1716-1728. DOI: 10.1021/ci8001414

7. Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Bull. Soc. Vaudoise Sci. Nat. 37, 547-579.

8. Medina-Franco, J.L. (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

9. Medina-Franco, J.L. (2013). Activity Cliffs: Facts or Artifacts? Chem. Biol. Drug Des., 81, 553-556. DOI: 10.1111/cbdd.12115.

10. Medina-Franco, J.L., Maggiora, G.M., Giulianotti, M.A., Pinilla, C. & Houghten, R.A. (2007). A Similarity-based Data-fusion Approach to the Visual Characterization and Comparison of Compound Databases. Chem. Biol. Drug Des., 70, 393-412. DOI: 10.1111/j.1747-0285.2007.00579.x

11. Medina-Franco, J.L., Martínez-Mayorga, K., Bender, A., Marín, R.M., Giulianotti, M.A., Pinilla, C. & Houghten, R.A. (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

12. Medina-Franco, J.L., Yongye, A.B. & López-Vallejo, F. (2012). Consensus Models of Activity Landscapes. In Statistical Modeling of Molecular Descriptors in QSAR/QSPR, 307-326 (Eds. Matthias, D., Kurt, V. and Danail, B.). Wiley-VCH. DOI: 10.1002/9783527645121.ch11

13. Medina-Franco, J.L., Yongye, A.B., Pérez-Villanueva ,J., Houghten, R.A. & 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

14. Méndez-Lucio, O., Pérez-Villanueva, J., Castillo, R. & Medina- Franco, J.L. (2012). Identifying Activity Cliff Generators of PPAR Ligands Using SAS Maps. Mol. Inf., 31, 837-846. DOI: 10.1002/minf.201200078

15. Peltason, L., Iyer, P. & Bajorath, J. (2010). Rationalizing Three- Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliff. J. Chem. Inf. Model., 50, 1021-1033. DOI: 10.1021/ci100091e

16. Pérez-Villanueva, J., Santos, R., Hernández-Campos, A., Giulianotti, M.A., Castillo, R., & Medina-Franco, J.L. (2010). Towards a systematic characterization of the antiprotozoal activity landscape of benzimidazole derivatives. Bioorg. Med. Chem., 18, 7380-7391. 32. DOI: 10.1016/j.bmc.2010.09.019 Sastry, M., Lowrie, J.F., Dixon, S.L. & Sherman, W. (2010). Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments. J. Chem. Inf. Model., 50, 771-784. DOI: 10.1021/ci100062n

17. Shanmugasundaram, V. & Maggiora, G.M. (2001).Characterizing property and activity landscapes using an information-theoretic approach. CINF-032. In 222nd ACS National Meeting, Chicago, IL, United States Chicago, IL, United States: American Chemical Society, Washington, D. C.

18. Waddell, J. & Medina-Franco, J.L. (2012). Bioactivity landscape modeling: chemoinformatic characterization of structure-activity relationships of compounds tested across multiple targets. Bioorg. Med. Chem. 20, 5443-5452. DOI: 10.1016/j.bmc.2011.11.051

19. Willett, P. (2013). Combination of Similarity Rankings Using Data Fusion. J. Chem. Inf. Model. 53, 1-10. DOI: 10.1021/ci300547g

20. Yongye, A.B. & Medina-Franco, J.L. (2012). Data Mining of Protein- Binding Profiling Data Identifies Structural Modifications that Distinguish Selective and Promiscuous Compound. J. Chem. Inf. Model., 52, 2454-2461. DOI: 10.1021/ci3002606

21. Yongye, A., Byler, K., Santos, R., Martínez-Mayorga, K., Maggiora, G.M. & Medina-Franco, J.L. (2011). Consensus Models of Activity Landscapes with Multiple Chemical, Conformer, and Property Representations. J. Chem. Inf. Model., 51, 1259-1270. DOI: 10.1021/ci200081k