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Gini G, Lorenzini M, Benfenati E, Brambilla R, Malvé L (2001) Mixing a symbolic and a subsymbolic expert to improve carcinogenicity prediction of aromatic compounds, LNCS 2096. Springer-­Verlag, New York 29. Gini G, Craciun M, Koening C, Benfenati E (2004) Combining unsupervised and supervised artificial neural networks to predict aquatic toxicity. J Chem Inf Comput Sci 44(6):1897–1902 30. Friedman J (1997) On bias, variance, 0/1 loss and the curse of dimensionality. Data Min Knowl Discov 1:55–77 31.

It is really necessary to test all of them on animals? Even more, it is necessary to synthetize them or would it be better to in silico assess their properties before making them, using a proactive strategy? The Declaration of Bologna, in 1999, called the 3 R (for Reduce, Refine, and Replace), proposed a manifesto to develop alternative methods that could save millions of animals. In this scenario, the ethical issues, however, are advocated also by authorities that have to protect humans and see the animals as a more ethical use than that of humans.

Be fast and conservative in decisions taking. Public, media, and opinion makers; they wants to be ­protected against risk at 100 %. Part of the population is strongly against the use of animal models. As we may understand, good and validated in silico models can attract agreement from multiple actors. We have to note that for QSAR systems the output of the model, despite its good predictive value, is not sufficient; documentation enabling the user to accept or not the prediction is necessary. Some European projects, as CALEIDOS and PROSIL, are working in this direction.

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