Journal of Computer-Aided Molecular Design

, Volume 32, Issue 4, pp 497–509 | Cite as

Sparse QSAR modelling methods for therapeutic and regenerative medicine

  • David A. Winkler


The quantitative structure–activity relationships method was popularized by Hansch and Fujita over 50 years ago. The usefulness of the method for drug design and development has been shown in the intervening years. As it was developed initially to elucidate which molecular properties modulated the relative potency of putative agrochemicals, and at a time when computing resources were scarce, there is much scope for applying modern mathematical methods to improve the QSAR method and to extending the general concept to the discovery and optimization of bioactive molecules and materials more broadly. I describe research over the past two decades where we have rebuilt the unit operations of the QSAR method using improved mathematical techniques, and have applied this valuable platform technology to new important areas of research and industry such as nanoscience, omics technologies, advanced materials, and regenerative medicine. This paper was presented as the 2017 ACS Herman Skolnik lecture.


Quantitative structure–activity relationships QSAR Machine learning Deep learning Sparse feature selection Regenerative medicine Skolnik award 



I would like to acknowledge the very talented members of my group, Frank Burden (my long-term collaborator in neural networks), Vidana, Epa, Anna Tarasova, Julianne Halley, Mitch Polley, Tu Le, and my current collaborators at CSIRO, Imperial College, MIT, and Nottingham. Their contributions are captured in the cited publications and I’m extremely grateful for their dedication and valuable intellectual contributions. I’ve also been very fortunate to have some excellent mentors during my career. I’m especially grateful to Prof. Toshio Fujita, Prof. Peter Andrews, and Prof. Graham Richards for valuable guidance and mentorship. I would also like to thank the ACS for the Herman Skolnik award and travel support, and the speakers in the Skolnik symposium for their great support. Support of UK Engineering and Physical Sciences Research Council (EPSRC) Grant EP/N006615/1 for the Programme Grant in Next Generation Biomaterials Discovery and a Monash University-Nottingham travelling fellowship are also gratefully acknowledged.


  1. 1.
    Mitchell M (2009) Complexity: a guided tour. Oxford University Press, OxfordGoogle Scholar
  2. 2.
    Halley JD, Winkler DA (2008) Complexity 14(2):10CrossRefGoogle Scholar
  3. 3.
    Halley JD, Winkler DA (2008) Complexity 13(5):10CrossRefGoogle Scholar
  4. 4.
    Bhadeshia HKDH. (1999) ISIJ Int 39(10):966CrossRefGoogle Scholar
  5. 5.
    Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA (2012) Nano Letters 12(11):5808CrossRefGoogle Scholar
  6. 6.
    Winkler DA, Burden FR (2012) Mol Biosyst 8(3):913CrossRefGoogle Scholar
  7. 7.
    Hansch C, Maloney PP, Fujita T (1962) Nature 194(4824):178CrossRefGoogle Scholar
  8. 8.
    Hansch C, Fujita T (1964) J Am Chem Soc 86(8):1616CrossRefGoogle Scholar
  9. 9.
    Fujita T, Winkler DA (2016) J Chem Inf Model 56(2):269CrossRefGoogle Scholar
  10. 10.
    Le T, Epa VC, Burden FR, Winkler DA (2012) Chem Rev 112(5):2889CrossRefGoogle Scholar
  11. 11.
    Gedeck P, Rohde B, Bartels C (2006) J Chem Inf Model 46(5):1924CrossRefGoogle Scholar
  12. 12.
    Clark M, Cramer RD (1993) Quant Struct Act Rel 12(2):137CrossRefGoogle Scholar
  13. 13.
    Alexander DLJ, Tropsha A, Winkler DA (2015) J Chem Inf Model 55(7):1316CrossRefGoogle Scholar
  14. 14.
    Hansch C, Fujita T (1995) ACS Sym Ser 606:1CrossRefGoogle Scholar
  15. 15.
    Kubinyi H (1990) J Cancer Res Clin 116(6):529CrossRefGoogle Scholar
  16. 16.
    Niculescu SP (2003) J Mol Struct 622(1–2):71CrossRefGoogle Scholar
  17. 17.
    Burden FR, Rosewarne BS, Winkler DA (1997) Chemometr Intell Lab Syst 38(2):127CrossRefGoogle Scholar
  18. 18.
    Burden FR, Winkler DA (1999) J Chem Inf Comput Sci 39(2):236CrossRefGoogle Scholar
  19. 19.
    Winkler D (2001) Drug Discov Today 6(23):1198CrossRefGoogle Scholar
  20. 20.
    Winkler DA (2004) Mol Biotechnol 27(2):139CrossRefGoogle Scholar
  21. 21.
    Burden FR, Polley MJ, Winkler DA (2009) J Chem Inf Model 49(3):710CrossRefGoogle Scholar
  22. 22.
    Winkler DA, Burden FR, Watkins AJR (1998) Quant Struct Act Rel 17(1):14CrossRefGoogle Scholar
  23. 23.
    Burden FR, Winkler DA (2005) J Mol Graph Model 23(6):481CrossRefGoogle Scholar
  24. 24.
    Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato J, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, RAdams RP, Aspuru-Guzik A (2018) ACS Cent Sci ASAP. Google Scholar
  25. 25.
    Hook AL, Chang CY, Yang J, Luckett J, Cockayne A, Atkinson S, Mei Y, Bayston R, Irvine DJ, Langer R, Anderson DG, Williams P, Davies MC, Alexander MR (2012) Nat Biotechnol 30(9):868CrossRefGoogle Scholar
  26. 26.
    Topliss JG, Costello RJ (1972) J Med Chem 15(10):1066CrossRefGoogle Scholar
  27. 27.
    Figueiredo MAT (2003) IEEE Trans Pattern Anal Mach Intell 25(9):1150CrossRefGoogle Scholar
  28. 28.
    Burden FR, Winkler DA (2009) QSAR Comb Sci 28(6–7):645CrossRefGoogle Scholar
  29. 29.
    Burden FR, Winkler DA (2009) Bayesian regularization of neural networks. In: Livingston D (ed) Artificial neural networks: methods and applications, vol 458. Humana Press, TotowaGoogle Scholar
  30. 30.
    Burden FR, Winkler DA (2015) J Chem Inf Model 55(8):1529CrossRefGoogle Scholar
  31. 31.
    Hornik K (1991) Neural Netw 4(2):251CrossRefGoogle Scholar
  32. 32.
    Burden FR, Winkler DA (2009) QSAR Comb Sci 28(10):1092CrossRefGoogle Scholar
  33. 33.
    Burden FR, Winkler DA (1999) J Med Chem 42(16):3183CrossRefGoogle Scholar
  34. 34.
    Winkler DA, Le TC (2017) Mol Inf 36:(1–2)Google Scholar
  35. 35.
    Burden FR, Ford MG, Whitley DC, Winkler DA (2000) J Chem Inf Comput Sci 40(6):1423CrossRefGoogle Scholar
  36. 36.
    Salahinejad M, Le TC, Winkler DA (2013) Mol Pharmaceut 10(7):2757CrossRefGoogle Scholar
  37. 37.
    Winkler DA (2016) Toxicol Appl Pharmacol 299:96CrossRefGoogle Scholar
  38. 38.
    Winkler DA, Mombelli E, Pietroiusti A, Tran L, Worth A, Fadeel B, McCall MJ (2013) Toxicology 313(1):15CrossRefGoogle Scholar
  39. 39.
    Mauri A, Consonni V, Pavan M, Todeschini R (2006) Match Commun Math Comput Sci 56(2):237Google Scholar
  40. 40.
    Epa VC, Hook AL, Chang C, Yang J, Langer R, Anderson DG, Williams P, Davies MC, Alexander MR, Winkler DA (2014) Adv Funct Mater 24(14):2085CrossRefGoogle Scholar
  41. 41.
    Mikulskis P, Hook AL, Alexander MH, Winkler DA (2018) ACS Appl Mater Interfaces 10(1):139–149CrossRefGoogle Scholar
  42. 42.
    Autefage H, Gentleman E, Littmann E, Hedegaard MAB, Von Erlach T, O’Donnell M, Burden FR, Winkler DA, Stevens MM (2015) Proc Natl Acad Sci USA 112(14):4280CrossRefGoogle Scholar
  43. 43.
    Cybenko G (1989) Math Control Signal Syst 2(4):303CrossRefGoogle Scholar
  44. 44.
    Le TC, Winkler DA (2015) ChemMedChem 10(8):1296CrossRefGoogle Scholar
  45. 45.
    Le TC, Winkler DA (2016) Chem Rev 116(10):6107CrossRefGoogle Scholar
  46. 46.
    Puentedura RR (2003) The Baldwin effect in the age of computation. In: Weber BH, Depew DJ (eds) Evolution and learning: the Baldwin effect reconsidered. MIT Press, CambridgeGoogle Scholar
  47. 47.
    Hinton GE, Nowlan SJ (1987) Complex Syst 1:495Google Scholar
  48. 48.
    Thornton AW, Simon CM, Kim J, Kwon O, Deeg KS, Konstas K, Pas SJ, Hill MR, Winkler DA, Haranczyk M, Smit B (2017) Chem Mater 29(7):2844CrossRefGoogle Scholar
  49. 49.
    Nowak-Sliwinska P, Weiss A, Ding X, Dyson PJ, van den Bergh H, Griffioen AW, Ho C-M (2016) Nat Protoc 11:302CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Monash Institute of Pharmaceutical SciencesMonash UniversityParkvilleAustralia
  2. 2.La Trobe Institute for Molecular ScienceLa Trobe UniversityBundooraAustralia
  3. 3.CSIRO ManufacturingClaytonAustralia
  4. 4.School of PharmacyUniversity of NottinghamNottinghamUK
  5. 5.School of Chemical and Physical SciencesFlinders UniversityBedford ParkAustralia

Personalised recommendations