How Far Chemistry and Toxicology are Computational Sciences?

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


In this chapter we describe the basis of computational chemistry and discuss how computational methods have been extended to biology, and toxicology in particular. Since about 20 years, chemical experimentation is more and more replaced by modelling and virtual experimentation. Computer modelling of biological properties is still a debated topic. However, the need of safety assessment of chemicals is pushing toxicology towards computer modelling. The term in silico discovery is now applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more towards computer modelling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.


In silico experiments Chemoinformatics QSAR Toxicology 


  1. 1.
    Lynch M (2004) Introduction of computers in chemical structure information systems, or what is not recorded in the annals. In: Proceedings of 2002 conference on the history and heritage of scientific and technological, information systems, pp 137–148Google Scholar
  2. 2.
    Brown N (2009) Chemoinformatics—An introduction for computer scientists. ACM Comput Surv 41(2):8:1–8:38Google Scholar
  3. 3.
    Gasteiger J, Engel T (2003) Chemoinformatics: a textbook. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  4. 4.
    Willett P, Barnard J, Downs G (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996CrossRefGoogle Scholar
  5. 5.
    Benfenati E, Gini G (1997) Computational predictive programs (expert systems) in toxicology. Toxicology 119:213–225CrossRefGoogle Scholar
  6. 6.
    Gini G, Katritzky A (Eds) (1999) Predictive toxicology of chemicals: experiences and impact of Artificial Intelligence tools. In: Proceedings of AAAI spring symposium on predictive, toxicology, SS-99-01Google Scholar
  7. 7.
    Hartung T (2009) Toxicology for the twenty-first century. Nature 460(9):208–212CrossRefGoogle Scholar
  8. 8.
    Scerri E (2006) The periodic table: its story and its significance. Oxford University Press, New YorkGoogle Scholar
  9. 9.
    Balaban A (1985) Applications of graph theory in chemistry. J Chem Inf Comput Sci 25:334–343Google Scholar
  10. 10.
    Weininger D (1988) SMILES, a chemical language and information system. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36CrossRefGoogle Scholar
  11. 11.
    Morgan HL (1965) The generation of a unique machine description for chemical structures—A technique developed at chemical abstracts service. J Chem Docum 5:107–113CrossRefGoogle Scholar
  12. 12.
    Gillet V, Willet P, Bradshaw J, Green D (1999) Selecting combinatorial libraries to optimize diversity and physical properties. J Chem Inf Comput Sci 39:169–177CrossRefGoogle Scholar
  13. 13.
    Chow P, Ng R, Ogden B (eds) (2008) Using animal model in biomedical research. World Scientific Publishing Co, SingaporeGoogle Scholar
  14. 14.
    Livingston D (2000) The characterization of chemical structures using molecular properties. A survey. J Chem Inf Comput Sci 40:195–209CrossRefGoogle Scholar
  15. 15.
    Hansch C, Malony P, Fujita T, Muir R (1962) Correlation of biological activity of phenoxyacetic acids with hammett substituent constants with partition coefficents. Nature 194: 178–180Google Scholar
  16. 16.
    Ghose A, Crippen G (1986) Atomic physicochemical parameters for three-dimensional structure directed quantitative structure-activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comp Chem 7:565–577CrossRefGoogle Scholar
  17. 17.
    Karelson M (2000) Molecular descriptors in QSAR/QSPR. Wiley-VCH, WeinheimGoogle Scholar
  18. 18.
    Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkCrossRefGoogle Scholar
  19. 19.
    Ferrari T, Gini G, Golbamaki Bakhtyari N, Benfenati E (2011) Mining structural alerts from SMILES: a new way to derive structure-activity relationships. In: Proceedings of 2011 IEEE CIDM, pp 120–127Google Scholar
  20. 20.
    Gini G, Benfenati E (2007) E-modelling: foundations and cases for applying AI to life sciences. Int J Artif Intell T 16(2):243–268CrossRefGoogle Scholar
  21. 21.
    Ashby J (1985) Fundamental SAs to potential carcinogenicity or noncarcinogenicity. Environ Mutagen 7:919–921CrossRefGoogle Scholar
  22. 22.
    Jorgensen W (2004) The many roles of computation in drug discovery. Science 303:1813–1818CrossRefGoogle Scholar
  23. 23.
    Lipinski C, Lombardo F, Dominy B, Feeney P (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26CrossRefGoogle Scholar
  24. 24.
    Cortes J, Jaillet L, Simeon T (2007) Molecular disassembly with RRT-like algorithms. In: Proceedings of 2007 IEEE ICRA, pp 3301–3306Google Scholar
  25. 25.
    Norvig P (2012), accessed July 2013
  26. 26.
    Breiman L (2001) Statistical modelling: the two cultures. Stat Sci 16(3):199–231MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Kalisch M, Mächler M, Colombo D, Maathuis M, Bühlmann P (2012) Causal inference using graphical models with the R package pcalg. J Stat Softw 47(11):1–26Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanItaly

Personalised recommendations