• Marta TeijeiraEmail author
  • María Celeiro


(Q)Structure-Activity Relationships (QSAR and SAR) studies have been widely used in Medicinal Chemistry as a support in the drug’s discovery and development process, as well as in the study of harmful and poisonous substances in Toxicological Chemistry. They have also been applied in other areas of the natural sciences as a tool for learning the behavior of biological systems, supporting the idea that the physiological effect of a compound is a function of its chemical structure So, SAR studies aim to extract relevant chemical information in series of chemical compounds that share a similar biological activity.


  1. Abe M, Nishikawa K, Fukuda H, Nakanishi K, Tazawa Y, Taniguchi T, Park SY, Hiradate S, Fujii Y, Okuda K, Shindo M (2012) Key structural features of cis-cinnamic acid as an allelochemical. Phytochemistry 84:56–67CrossRefPubMedCentralPubMedGoogle Scholar
  2. Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797CrossRefPubMedGoogle Scholar
  3. Armitage JE, Lynch MF (1967) Automatic detection of structural similarities among chemical compounds. J Chem Soc C Org:521–528Google Scholar
  4. Avram S, Funar-Timofei S, Borota A, Chennamaneni SR, Manchala AK, Muresan S (2014) Quantitative estimation of pesticide-likeness for agrochemical discovery. J Chem Inform 6:1–11Google Scholar
  5. Bajorath J (2017) Representation and identification of activity cliffs. Expert Opin Drug Discovery 12:879–883CrossRefGoogle Scholar
  6. Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Chem Inform 7:1–13Google Scholar
  7. Barakat N, Bradley AP (2010) Rule extraction from support vector machines: a review. Neurocomputing 74:178–190CrossRefGoogle Scholar
  8. Barigye SJ, Duarte MH, Nunes CA, Freitas MP (2016) MIA-plot: a graphical tool for viewing descriptor contributions in MIA-QSAR. RSC Adv 6:49604–49612CrossRefGoogle Scholar
  9. Bender A, Jenkins JL, Scheiber J, Sukuru SCK, Glick M, Davies JW (2009) How similar are similarity searching methods ? A principal component analysis of molecular descriptor space. J Chem Inf Model 49:108–119CrossRefPubMedGoogle Scholar
  10. Cartwright H (2015) Artificial neural networks. Springer, New YorkCrossRefGoogle Scholar
  11. Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G (2015) Molecular fingerprint similarity search in virtual screening. Methods 71:58–63CrossRefPubMedCentralPubMedGoogle Scholar
  12. Chakraborty S, Basu S (2014) Mechanistic insight into the radical scavenging activity of polyphenols and its application in virtual screening of phytochemical library: an in silico approach. Eur Food Res Technol 239:885–893CrossRefGoogle Scholar
  13. Cimmino A, Masi M, Evidente M, Superchi S, Evidente A (2015) Fungal phytotoxins with potential herbicidal activity: chemical and biological characterization. Nat Prod Rep 32:1629–1653CrossRefPubMedCentralPubMedGoogle Scholar
  14. Duesbury E, Holliday J, Willett P (2017) Comparison of maximum common subgraph isomorphism algorithms for the alignment of 2D chemical structures. Chem Med Chem.
  15. Englert P, Kovács P (2015) Efficient heuristics for maximum common substructure search. J Chem Inf Model 55:941–955CrossRefPubMedCentralPubMedGoogle Scholar
  16. Evidente A, Adolfi A, Cimmino A (2011) Relationships between the stereochemistry and biological activity of fungal phytotoxins. Chirality 23:674–693CrossRefPubMedGoogle Scholar
  17. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular docking and structure-based drug design strategies. Molecules 20:13384–13421CrossRefGoogle Scholar
  18. Fourches D, Muratov E, Tropsha A (2010) Trust but verify: on the importance of chemical structure curation in chemoinformatics and QSAR modeling research. J Chem Inf Model 50:1189–1204CrossRefPubMedCentralPubMedGoogle Scholar
  19. Freitas MR, Matias SVBG, Macedo RLG, Freitas MP, Venturin N (2013) Augmented multivariate image analysis applied to quantitative structure-activity relationship modeling of the phytotoxicities of benzoxazinone herbicides and related compounds on problematic weeds. J Agric Food Chem 61:8499–8503CrossRefPubMedGoogle Scholar
  20. Gajewicz A (2018) How to judge whether QSAR/read-across predictions can be trusted? Novel approach for establishing model’s applicability domain. Environ Sci Nano 14.
  21. Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50:205–216CrossRefPubMedCentralPubMedGoogle Scholar
  22. Guha R, Van Drie JH (2008a) Structure – activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 48:646–658CrossRefPubMedCentralPubMedGoogle Scholar
  23. Guha R, Van Drie JH (2008b) Assessing how well a modeling protocol captures a structure-activity landscape. J Chem Inf Model 48:1716–1728CrossRefPubMedCentralPubMedGoogle Scholar
  24. Hu Y, Stumpfe D, Bajorath J (2011) Lessons learned from molecular scaffold analysis. J Chem Inf Model 51:1742–1753CrossRefPubMedCentralPubMedGoogle Scholar
  25. Iyer P, Dimova D, Vogt M, Bajorath J (2012) Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map. J Chem Inf Model 52:1962–1969CrossRefPubMedCentralPubMedGoogle Scholar
  26. Jhin C, Hwang KT (2015) Adaptive neuro-fuzzy inference system applied qsar with quantum chemical descriptors for predicting radical scavenging activities of carotenoids. PLoS One 10:1–13CrossRefGoogle Scholar
  27. Jiao L, Zhang X, Qin Y, Wang X, Li H (2016) Hologram QSAR study on the electrophoretic mobility of aromatic acids. Chemom Intell Lab Syst 157:202–207CrossRefGoogle Scholar
  28. Klopmand G (1992) In: Johnson MA, Maggiora GM (eds) Concepts and applications of molecular similarity. Wiley, New York 1990, J Comput Chem 13:539–540Google Scholar
  29. Liu P, Long W (2009) Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 10:1978–1998CrossRefPubMedCentralPubMedGoogle Scholar
  30. Macías FA, Marín D, Oliveros-Bastidas A, Castellano D, Simonet AM, Molinillo JMG (2006) Structure-activity relationship (SAR) studies of benzoxazinones, their degradation products, and analogues. Phytotoxicity on problematic weeds Avena fatua L. and Lolium rigidum Gaud. J Agric Food Chem 54:1040–1048CrossRefPubMedCentralPubMedGoogle Scholar
  31. McKinney JD (2000) The practice of structure activity relationships (SAR) in toxicology. Toxicol Sci 56:8–17CrossRefPubMedGoogle Scholar
  32. Mishra AK, Tyagi C, Pandey B, Chakraborty O, Kumar A, Jain AK (2016) Structural insights into the mode of action of plant flavonoids as anti-oxidants using regression analysis. Proc Natl Acad Sci 86:1023–1036Google Scholar
  33. Nagarajan M, Maruthanayagam V, Sundararaman M (2013) SAR analysis and bioactive potentials of freshwater and terrestrial cyanobacterial compounds: a review. J Appl Toxicol 33:313–349CrossRefPubMedGoogle Scholar
  34. Pan L, Li X, Jin H, Yang X, Qin B (2017) Antifungal activity of umbelliferone derivatives: synthesis and structure-activity relationships. Microb Pathog 104:110–115CrossRefPubMedCentralPubMedGoogle Scholar
  35. Peltason L, Bajorath J (2008) Molecular similarity analysis in virtual screening. In: Varnek A, Tropsha A (eds) Chemoinformatics approaches to virtual screening. The Royal Society of Chemistry Publishing, Cambridge, UK, pp 120–149CrossRefGoogle Scholar
  36. Perez Gonzalez M, Teran C, Saiz-Urra L, Teijeira M (2008) Variable selection methods in QSAR: an overview. Curr Top Med Chem 8:1606–1627CrossRefGoogle Scholar
  37. Puzyn T, Leszczynski J, Cronin MTD (2010) Recent advances in QSAR Studies. Methods and applications. Springer, New York 423 ppCrossRefGoogle Scholar
  38. Rocher F, Roblin G, Chollet JF (2017) Modifications of the chemical structure of phenolics differentially affect physiological activities in pulvinar cells of Mimosa pudica L. II. Influence of various molecular properties in relation to membrane transport. Environ Sci Pollut Res 24:6910–6922CrossRefGoogle Scholar
  39. Rognan D (2011) Docking methods for virtual screening: principles and recent advances. In: Sotriffer C, Mannhold R, Kubinyi H, Folkers G (eds) Virtual screening: principles, challenges, and practical guidelines. Wiley-VCH, Weinheim, pp 153–176CrossRefGoogle Scholar
  40. Roy K, Ambure P, Kar S, Ojha PK (2018) Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J Chemom e2992.
  41. Ruiz IL, García GC, Angel M (2012) Structural-similarity-based approaches for the development of clustering and QSPR / QSAR Models in chemical databases. In: Dehmer M, Varmuza K, Bonchev D, Emmert-Streib F (eds) Statistical modelling of molecular descriptors in QSAR/QSPR. Wiley-VCH Verlag GmbH & Co. KGaA, UKGoogle Scholar
  42. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791–4810CrossRefPubMedGoogle Scholar
  43. Satpathy R, Guru RK, Behera R (2010) Computational QSAR analysis of some physiochemical and topological descriptors of curcumin derivatives by using different statistical methods. J Chem Pharm Res 2:344–350Google Scholar
  44. Shaikh AR, Gonsalves SI, Nikam A, Kshirsagar SJ, Thombare Y (2015) Predicting pyrazinecarboxamides derivatives as an herbicidal agent: 3d Qsar by kNN-MFA and multiple linear regression approach. World Appl Sci J 33:980–989Google Scholar
  45. Shanmugam G, Jeon J (2017) Aided drug discovery in plant pathology. Plant Pathol J 33:529–542PubMedPubMedCentralGoogle Scholar
  46. Sliwoski G, Kothiwale S, Meiler J, Lowe EWE (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395CrossRefPubMedCentralPubMedGoogle Scholar
  47. Speck-Planche A, Kleandrova VV, Rojas-Vargas JA (2011) QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol Divers 15:901–909CrossRefPubMedGoogle Scholar
  48. Stumpfe D, Bajorath J (2012) Methods for SAR visualization. RSC Adv 2:369–378CrossRefGoogle Scholar
  49. Stumpfe D, Hu Y, Dimova D, Bajorath J (2014) Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem 57:18–28CrossRefPubMedGoogle Scholar
  50. Sukumar N, Das S, Krein M, Godawat R, Vitol I, Garde S, Bennett K, Breneman CM (2012) Molecular descriptors for biological systems. In: Guha R, Bender A (eds) Computational approaches in cheminformatics and bioinformatics. Wiley-VCH, Weinheim, pp 107–143Google Scholar
  51. Terfloth L (2003) Calculation of structure descriptors. In: Engel JG (ed) Chemo-informatics. Wyley-VCH, Weinheim, pp 401–437Google Scholar
  52. Tobias RD (1995) An introduction to partial least squares regression. SAS Conf Proc SAS Users Gr Int 20 (SUGI 20) 2–5Google Scholar
  53. Todeschini R, Consonni V, Mannhold R, Kubinyi H, Folkers G (2009) Molecular descriptors for chemoinformatics, vol I & II. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  54. Todeschini R, Consonni V, Xiang H, Holliday J, Buscema M, Willett P (2012) Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J Chem Inf Model 52:2884–2901CrossRefPubMedCentralPubMedGoogle Scholar
  55. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488CrossRefPubMedCentralPubMedGoogle Scholar
  56. Tropsha A, Gramatica P, Gombar V (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77CrossRefGoogle Scholar
  57. Vedani A, Dobler M (2002) 5D-QSAR: the key for simulating induced fit? J Med Chem 45:2139–2149CrossRefPubMedCentralPubMedGoogle Scholar
  58. Wassermann AM, Bajorath J (2011) A data mining method to facilitate SAR transfer. J Chem Inf Model 51:1857–1866CrossRefPubMedCentralPubMedGoogle Scholar
  59. Wassermann AM, Peltason L, Bajorath J (2010) Computational analysis of multi-target structure-activity relationships to derive preference orders for chemical modifications toward target selectivity. ChemMedChem 5:847–858CrossRefPubMedCentralPubMedGoogle Scholar
  60. Wawer MJ, Jaramillo DE, Dancik V, Fass DM, Stephen J, Shamji AF, Wagner BK, Schreiber SL, Paul A (2014) Automated structure–activity relationship mining: connecting chemical structure to biological profiles. J Biomol Screen 19:738–748CrossRefPubMedCentralPubMedGoogle Scholar
  61. Willett P (2014) The calculation of molecular structural similarity: principles and practice. Mol Inform 33:403–413CrossRefPubMedCentralPubMedGoogle Scholar
  62. Xue CX, Zhang XY, Liu MC, Hu ZD, Fan BT (2005) Study of probabilistic neural networks to classify the active compounds in medicinal plants. J Pharm Biomed Anal 38:497–507CrossRefPubMedCentralPubMedGoogle Scholar
  63. Young D, Martin T, Venkatapathy R, Harten P (2008) Are the chemical structures in your QSAR correct? QSAR Comb Sci 27:1337–1345CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de Química Orgánica. Facultade de QuímicaUniversidade de VigoVigoSpain
  2. 2.Instituto de Investigación Sanitaria Galicia Sur (IISGS)Universidade de VigoVigoSpain

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