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Towards the Revival of Interpretable QSAR Models

  • Watshara Shoombuatong
  • Philip Prathipati
  • Wiwat Owasirikul
  • Apilak Worachartcheewan
  • Saw Simeon
  • Nuttapat Anuwongcharoen
  • Jarl E. S. Wikberg
  • Chanin Nantasenamat
Chapter
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 24)

Abstract

Quantitative structure-activity relationship (QSAR) has been instrumental in aiding medicinal chemists and physical scientists in understanding how modification of substituents at different positions on a molecular structure exert its influence on the observed biological activity and physicochemical property, respectively. QSAR has received great attention owing to its predictive capability and as such efforts had been directed toward obtaining models with high prediction performance. However, to be useful QSAR models need to be informative and interpretable in which the underlying molecular features that contribute to the increase or decrease of the biological activity are revealed by the model. Thus, the aim of this chapter is to briefly review the general concepts of QSAR modeling, its development and discussions on key issues influencing and contributing to the interpretability of QSAR models.

Keywords

Quantitative structure-activity relationship Quantitative structure-property relationship Proteochemometrics Data mining Machine learning Cheminformatics Chemogenomics QSAR QSPR Interpretable Drug discovery Drug design 

Notes

Acknowledgements

This work is supported by a Research Career Development Grant (No. RSA5780031) to CN from the Thailand Research Fund; the New Scholar Research Grant (No. MRG5980220) to WS from the Thailand Research Fund; and the Swedish Research Links program (No. C0610701) to CN and JESW from the Swedish Research Council.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Watshara Shoombuatong
    • 1
  • Philip Prathipati
    • 2
  • Wiwat Owasirikul
    • 3
  • Apilak Worachartcheewan
    • 4
  • Saw Simeon
    • 1
  • Nuttapat Anuwongcharoen
    • 1
  • Jarl E. S. Wikberg
    • 5
  • Chanin Nantasenamat
    • 1
  1. 1.Center of Data Mining and Biomedical InformaticsFaculty of Medical Technology, Mahidol UniversityBangkokThailand
  2. 2.National Institutes of Biomedical Innovation, Health and NutritionOsakaJapan
  3. 3.Department of Radiological Technology, Faculty of Medical TechnologyMahidol UniversityBangkokThailand
  4. 4.Department of Community Medical Technology, Faculty of Medical TechnologyMahidol UniversityBangkokThailand
  5. 5.Department of Pharmaceutical Biosciences, BMCUppsala UniversityUppsalaSweden

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