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Improving genetic programming for the prediction of pharmacokinetic parameters

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Abstract

The prediction of pharmacokinetic parameters is a crucial phase of the drug discovery process, and the automatization of this task is a hot topic in computational bio-medicine. In the last 10 years, a significant amount of research has been published reporting on applications of genetic programming to the prediction of pharmacokinetic parameters. This paper summarizes and discusses some of those contributions. In particular, the focus is on the idea that pharmacokinetic problems are so complex that the “canonic” version of genetic programming is often not able to perform appropriately on them. At the same time, genetic programming has a high degree of versatility, given by the opportunity it offers of adapting many crucial parts of its algorithm, among which the fitness function and the employed genetic operators. This gives us the chance to improve standard genetic programming in several different ways. For instance, sophisticated fitness functions, methods to control bloat and operators to exploit the geometry of the semantic space are discussed here.

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Fig. 1

Notes

  1. From now on, only the median (calculated over a set of independent runs, each of which performed using a different training/test set partition) of the results obtained on the test set are discussed. The interested reader is referred to the papers quoted in the text for a detailed discussion of all the experimental settings, including number of runs, used parameter values, etc.

  2. With this expression, here and in the continuation of the paper, it is intended that the differences between the compared methods are not statistically significant according to the Wilcoxon rank-sum test. This method has always been used with Bonferroni correction whenever the number of compared methods was larger than two. Also, this method has always been executed after verifying that data are not normally distributed using the Kolmogorov-Smirnov test, a result that consistently holds for all the results discussed in this paper.

  3. By the term “better”, here and in the continuation of the paper, it is meant that the differences between the compared methods are statistically significant according to the statistical test described in the previous footnote.

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Acknowledgments

I sincerely thank all the collaborators that worked with me on this research track in the last decade. In particular, my heartfelt acknowledge goes to Sara Silva, Mauro Castelli, Luca Manzoni and Francesco Archetti. I also acknowledge project MassGP (PTDC/EEI-CTP/2975/2012), FCT (Portugal), for financial support.

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Vanneschi, L. Improving genetic programming for the prediction of pharmacokinetic parameters. Memetic Comp. 6, 255–262 (2014). https://doi.org/10.1007/s12293-014-0143-9

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