Quantitative structure–activity relationship

Quantitative structure–activity relationship (QSAR) models are regression or classification models used in the chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable.

In QSAR modeling, the predictors consist of physico-chemical properties or theoretical molecular descriptors[1][2] of chemicals; the QSAR response-variable could be a biological activity of the chemicals. QSAR models first summarize a supposed relationship between chemical structures and biological activity in a data-set of chemicals. Second, QSAR models predict the activities of new chemicals.[3][4]

Related terms include quantitative structure–property relationships (QSPR) when a chemical property is modeled as the response variable.[5][6] "Different properties or behaviors of chemical molecules have been investigated in the field of QSPR. Some examples are quantitative structure–reactivity relationships (QSRRs), quantitative structure–chromatography relationships (QSCRs) and, quantitative structure–toxicity relationships (QSTRs), quantitative structure–electrochemistry relationships (QSERs), and quantitative structure–biodegradability relationships (QSBRs)."[7]

As an example, biological activity can be expressed quantitatively as the concentration of a substance required to give a certain biological response. Additionally, when physicochemical properties or structures are expressed by numbers, one can find a mathematical relationship, or quantitative structure-activity relationship, between the two. The mathematical expression, if carefully validated,[8][9][10][11] can then be used to predict the modeled response of other chemical structures.[12]

A QSAR has the form of a mathematical model:

  • Activity = f (physiochemical properties and/or structural properties) + error

The error includes model error (bias) and observational variability, that is, the variability in observations even on a correct model.

  1. ^ Todeschini, Roberto; Consonni, Viviana (2009). Molecular Descriptors for Chemoinformatics. Methods and Principles in Medicinal Chemistry. Vol. 41. Wiley. doi:10.1002/9783527628766. ISBN 978-3-527-31852-0.
  2. ^ Mauri, Andrea; Consonni, Viviana; Todeschini, Roberto (2017). "Molecular Descriptors". Handbook of Computational Chemistry. Springer International Publishing. pp. 2065–2093. doi:10.1007/978-3-319-27282-5_51. ISBN 978-3-319-27282-5.
  3. ^ Roy K, Kar S, Das RN (2015). "Chapter 1.2: What is QSAR? Definitions and Formulism". A primer on QSAR/QSPR modeling: Fundamental Concepts. New York: Springer-Verlag Inc. pp. 2–6. ISBN 978-3-319-17281-1.
  4. ^ Ghasemi, Pérez-Sánchez; Mehri, Pérez-Garrido (2018). "Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks". Drug Discovery Today. 23 (10): 1784–1790. doi:10.1016/j.drudis.2018.06.016. PMID 29936244. S2CID 49418479.
  5. ^ Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2009). "A practical overview of quantitative structure-activity relationship". Excli Journal. 8: 74–88. doi:10.17877/DE290R-690.
  6. ^ Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (Jul 2010). "Advances in computational methods to predict the biological activity of compounds". Expert Opinion on Drug Discovery. 5 (7): 633–54. doi:10.1517/17460441.2010.492827. PMID 22823204. S2CID 17622541.
  7. ^ Yousefinejad S, Hemmateenejad B (2015). "Chemometrics tools in QSAR/QSPR studies: A historical perspective". Chemometrics and Intelligent Laboratory Systems. 149, Part B: 177–204. doi:10.1016/j.chemolab.2015.06.016.
  8. ^ Tropsha A, Gramatica P, Gombar VJ (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. doi:10.1002/qsar.200390007.
  9. ^ Gramatica P (2007). "Principles of QSAR models validation: internal and external". QSAR Comb. Sci. 26 (5): 694–701. doi:10.1002/qsar.200610151. hdl:11383/1668881.
  10. ^ Ruusmann, V.; Sild, S.; Maran, U. (2015). "QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models". Journal of Cheminformatics. 7 32. doi:10.1186/s13321-015-0082-6. PMC 4479250. PMID 26110025.
  11. ^ Chirico N, Gramatica P (Aug 2012). "Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection". Journal of Chemical Information and Modeling. 52 (8): 2044–58. doi:10.1021/ci300084j. PMID 22721530.
  12. ^ Tropsha, Alexander (2010). "Best Practices for QSAR Model Development, Validation, and Exploitation". Molecular Informatics. 29 (6–7): 476–488. doi:10.1002/minf.201000061. ISSN 1868-1743. PMID 27463326. S2CID 23564249.
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