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On Exploring Structure–Activity Relationships

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In Silico Models for Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 993))

Abstract

Understanding structure–activity relationships (SARs) for a given set of molecules allows one to rationally explore chemical space and develop a chemical series optimizing multiple physicochemical and biological properties simultaneously, for instance, improving potency, reducing toxicity, and ensuring sufficient bioavailability. In silico methods allow rapid and efficient characterization of SARs and facilitate building a variety of models to capture and encode one or more SARs, which can then be used to predict activities for new molecules. By coupling these methods with in silico modifications of structures, one can easily prioritize large screening decks or even generate new compounds de novo and ascertain whether they belong to the SAR being studied. Computational methods can provide a guide for the experienced user by integrating and summarizing large amounts of preexisting data to suggest useful structural modifications. This chapter highlights the different types of SAR modeling methods and how they support the task of exploring chemical space to elucidate and optimize SARs in a drug discovery setting. In addition to considering modeling algorithms, I briefly discuss how to use databases as a source of SAR data to inform and enhance the exploration of SAR trends. I also review common modeling techniques that are used to encode SARs, recent work in the area of structure–activity landscapes, the role of SAR databases, and alternative approaches to exploring SAR data that do not involve explicit model development.

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References

  1. Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure based drug design: a molecular modeling perspective. Med Res Rev 16(1):3–50

    Article  PubMed  CAS  Google Scholar 

  2. Nicolotti O, Gillet VJ, Fleming PJ, Green DVS (2002) Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. J Med Chem 23:5069–5080

    Article  Google Scholar 

  3. Cruz-Monteagudo M, Borges F, Cordeiro MN (2008) Desirability-based multiobjective optimization for global QSAR studies: application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles. J Comput Chem 29(14):2445–2459

    Article  PubMed  CAS  Google Scholar 

  4. Nicolotti O, Giangreco I, Miscioscia TF, Carotti A (2009) Improving quantitative structure-activity relationships through multiobjective optimization. J Chem Inf Model 49(10):2290–2302

    Article  PubMed  CAS  Google Scholar 

  5. Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High Throughput Screen 9(3):213–228

    Article  PubMed  CAS  Google Scholar 

  6. Winkler DA (2002) The role of quantitative structure–activity relationships (QSAR) in biomolecular discovery. Brief Bioinform 3(1):73–86

    Article  PubMed  CAS  Google Scholar 

  7. Zvinavashe E, Murk AJ, Rietjens M (2008) Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity. Chem Res Toxicol 21(12):2229–2236

    Article  PubMed  CAS  Google Scholar 

  8. Banerjee A, Schepmann D, Kurthwein J et al (2010) Synthesis and SAR studies of chiral non-racemic dexoxadrol analogues as uncompetitive NMDA receptor antagonists. Bioorg Med Chem 18(22):7855–7867

    Article  PubMed  CAS  Google Scholar 

  9. Breiman L (2001) Statistical modeling: two cultures. Stat Sci 16:199–231

    Article  Google Scholar 

  10. Guha R (2008) On the interpretation and interpretability of quantitative structure-activity relationship models. J Comput Aided Mol Des 22(12):857–871

    Article  PubMed  CAS  Google Scholar 

  11. Stanton DT (2003) On the physical interpretation of QSAR models. J Chem Inf Comput Sci 43(5):1423–1433

    Article  PubMed  CAS  Google Scholar 

  12. Guha R, Jurs PC (2004) The development of QSAR models to predict and interpret the biological activity of artemisinin analogues. J Chem Inf Comput Sci 44:1440–1449

    Article  PubMed  CAS  Google Scholar 

  13. Guha R, Stanton DT, Jurs PC (2005) Interpreting computational neural network QSAR models: a detailed interpretation of the weights and biases. J Chem Inf Model 45:1109–1121

    Article  PubMed  CAS  Google Scholar 

  14. Segall M, Champness E, Obrezanova O, Leeding C (2009) Beyond profiling: using ADMET models to guide decisions. Chem Biodivers 6(11):2144–2151

    Article  PubMed  CAS  Google Scholar 

  15. Faulon JL, Visco DP Jr, Pophale RS (2003) The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J Chem Inf Comput Sci 43:707–720

    Article  PubMed  CAS  Google Scholar 

  16. Churchwell CJ, Rintoul MD, Martin S et al (2004) The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides. J Mol Graph Model 22(4):263–273

    Article  PubMed  CAS  Google Scholar 

  17. Wong WW, Burkowski FJ (2009) A constructive approach for discovering new drug leads: using a kernel methodology for the inverse-QSAR problem. J Cheminform 1:s4

    Article  Google Scholar 

  18. Weaver S, Gleeson MP (2008) The importance of the domain of applicability in QSAR modeling. J Mol Graph Model 26(8):1315–1326

    Article  PubMed  CAS  Google Scholar 

  19. Schultz TW, Hewitt M, Netzeva TI, Cronin MTD (2007) Assessing applicability domains of toxicological QSARs: definition, confidence in predicted values, and the role of mechanisms of action. QSAR Comb Sci 26:238–254

    Article  CAS  Google Scholar 

  20. Roberts DW, Patlewicz G, Kern PS et al (2007) Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization. Chem Res Toxicol 20(7):1019–1030

    Article  PubMed  CAS  Google Scholar 

  21. Stanforth RW, Kolossov E, Mirkin B (2007) A measure of domain of applicability for QSAR modelling based on intelligent K-means clustering. QSAR Comb Sci 26(7):837–844

    Article  CAS  Google Scholar 

  22. Tetko IV, Bruneau P, Mewes HW et al (2006) Can we estimate the accuracy of ADME-tox predictions? Drug Discov Today 11(15–16):700–707

    Article  PubMed  CAS  Google Scholar 

  23. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set in descriptor space: a review. Altern Lab Anim 33:445–459

    PubMed  CAS  Google Scholar 

  24. Sheridan RP, Feuston BP, Maiorov VN, Kearsley SK (2004) Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J Chem Inf Comput Sci 44(6):1912–1928

    Article  PubMed  CAS  Google Scholar 

  25. Xu YJ, Gao H (2003) Dimension related distance and its application in QSAR/QSPR model error estimation. QSAR Comb Sci 22:422–429

    Article  CAS  Google Scholar 

  26. Cook RD (1977) Detecting influential observations in linear regression. Technometrics 19:15–18

    Article  Google Scholar 

  27. Chatterjee S, Hadi AS (1986) Influential observations, high leverage points, and outliers in linear regression. Stat Sci 1(3):379–416

    Article  Google Scholar 

  28. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  29. Eriksson L, Jaworska J, Worth AP et al (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111(10):1361–1375

    Article  PubMed  CAS  Google Scholar 

  30. Papa E, Villa F, Gramatica P (2005) Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow). J Chem Inf Model 45(5):1256–1266

    Article  PubMed  CAS  Google Scholar 

  31. Nikolova-Jeliazkova N, Jaworska J (2005) An approach to determining AD for QSAR group contribution models: an analysis of SRC KOWWIN. Altern Lab Anim 33:461–470

    PubMed  CAS  Google Scholar 

  32. Brown N, Lewis RA (2006) Exploiting QSAR methods in lead optimization. Curr Opin Drug Discov Devel 9(4):419–424

    PubMed  CAS  Google Scholar 

  33. Lajiness MS (1991) Evaluation of the performance of dissimilarity selection methodology. In: Silipo C, Vittoria A (eds) QSAR: rational approaches to the design of bioactive compounds. Proceedings of the VIII European symposium on quantitative structure-activity relationships. Sorrento, Italy, 9–13 Sept 1990. ESCOM, Leiden, pp 201–204

    Google Scholar 

  34. Maggiora GM (2006) On outliers and activity cliffs—why QSAR often disappoints. J Chem Inf Model 46(4):1535

    Article  PubMed  CAS  Google Scholar 

  35. Shanmugasundaram V, Maggiora GM (2001) Characterizing property and activity land-scapes using an information-theoretic approach. In: CINF-032. 222nd National Meeting of the American Chemical Society Chicago, IL, Washington, DC

    Google Scholar 

  36. Medina-Franco JL, Martínez-Mayorga K, Bender A et al (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49(2):477–491

    Article  PubMed  CAS  Google Scholar 

  37. Yongye AB, Byler K, Santos R et al (2011) Consensus models of activity landscapes with multiple chemical, conformer, and property representations. J Chem Inf Model 51(6):1259–1270

    Article  PubMed  CAS  Google Scholar 

  38. Guha R, Van Drie JH (2008) The structure-activity landscape index: identifying and quantifying activity-cliffs. J Chem Inf Model 48(3):646–658

    Article  PubMed  CAS  Google Scholar 

  39. Peltason L, Bajorath J (2007) SAR index: quantifying the nature of structure-activity relationships. J Med Chem 50(23):5571–5578

    Article  PubMed  CAS  Google Scholar 

  40. Wawer M, Peltason L, Weskamp N et al (2008) Structure-activity relationship anatomy by network-like similarity graphs and local structure-activity relationship indices. J Med Chem 51(19):6075–6084

    Article  PubMed  CAS  Google Scholar 

  41. Wawer M, Peltason L, Bajorath J (2009) Elucidation of structure-activity relationship pathways in biological screening data. J Chem Inf Model 52(4):1075–1080

    CAS  Google Scholar 

  42. Seebeck B, Wagener M, Rarey M (2011) From activity cliffs to target-specific scoring models and pharmacophore hypotheses. ChemMedChem 6(9):1630–1639

    Article  PubMed  CAS  Google Scholar 

  43. Agrafiotis DK, Wiener JJM, Skalkin A, Kolpak J (2011) Single r-group polymorphisms (SRPs) and r-cliffs: an intuitive framework for analyzing and visualizing activity cliffs in a single analog series. J Chem Inf Model 51(5):1122–1131

    CAS  Google Scholar 

  44. Sisay MT, Peltason L, Bajorath J (2009) Structural interpretation of activity cliffs revealed by systematic analysis of structure-activity relationships in analog series. J Chem Inf Model 49(10):2179–2189

    Article  PubMed  CAS  Google Scholar 

  45. Austin CP, Brady LS, Insel TR, Collins FS (2004) NIH molecular libraries initiative. Science 306:1138–1139

    Article  PubMed  CAS  Google Scholar 

  46. Novotarskyi S, Sushko I, Körner R et al (2011) A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. J Chem Inf Model 51(6):1271–1280

    Article  PubMed  CAS  Google Scholar 

  47. Shen M-Y, Su B-H, Esposito EX et al (2011) A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets. Chem Res Toxicol 24(6):934–949

    Article  PubMed  CAS  Google Scholar 

  48. Chen B, Wild DJ (2010) PubChem BioAssays as a data source for predictive models. J Mol Graph Model 28(5):420–426

    Article  PubMed  CAS  Google Scholar 

  49. Blum LC, Reymond J-L (2009) 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131(25):8732–8733

    Article  PubMed  CAS  Google Scholar 

  50. Blum LC, van Deursen R, Bertrand S et al (2011) Discovery of α7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. J Chem Inf Model 51(12):3105–3112

    Article  PubMed  CAS  Google Scholar 

  51. Irwin JI, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182

    Article  PubMed  CAS  Google Scholar 

  52. von Korff M, Sander T (2006) Toxicity-indicating structural patterns. J Chem Inf Model 46:536–544

    Article  Google Scholar 

  53. Agrafiotis DK, Wiener JJM (2010) Scaffold explorer: an interactive tool for organizing and mining structure-activity data spanning multiple chemotypes. J Med Chem 53(13):5002–5011

    Article  PubMed  CAS  Google Scholar 

  54. Wetzel S, Klein K, Renner S et al (2009) Interactive exploration of chemical space with Scaffold Hunter. Nat Chem Biol 5(8):581–583

    Article  PubMed  CAS  Google Scholar 

  55. Jain AN, Cleves AE (2011) Does your model weigh the same as a duck? J Comput Aided Mol Des 26(1):57–67

    Article  PubMed  Google Scholar 

  56. Cramer RD (2011) The inevitable QSAR renaissance. J Comput Aided Mol Des 26(1):35–38

    Article  PubMed  Google Scholar 

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Guha, R. (2013). On Exploring Structure–Activity Relationships. In: Kortagere, S. (eds) In Silico Models for Drug Discovery. Methods in Molecular Biology, vol 993. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-342-8_6

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  • DOI: https://doi.org/10.1007/978-1-62703-342-8_6

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-341-1

  • Online ISBN: 978-1-62703-342-8

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