Genetic programming for computational pharmacokinetics in drug discovery and development

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@Article{Archetti:2007:GPEM,
  author =       "Francesco Archetti and Stefano Lanzeni and 
                 Enza Messina and Leonardo Vanneschi",
  title =        "Genetic programming for computational pharmacokinetics
                 in drug discovery and development",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2007",
  volume =       "8",
  number =       "4",
  pages =        "413--432",
  month =        dec,
  note =         "special issue on medical applications of Genetic and
                 Evolutionary Computation",
  keywords =     "genetic algorithms, genetic programming, Computational
                 pharmacokinetics, Drug discovery, QSAR",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-007-9040-z",
  abstract =     "The success of a drug treatment is strongly correlated
                 with the ability of a molecule to reach its target in
                 the patient's organism without inducing toxic effects.
                 Moreover the reduction of cost and time associated with
                 drug discovery and development is becoming a crucial
                 requirement for pharmaceutical industry. Therefore
                 computational methods allowing reliable predictions of
                 newly synthesised compounds properties are of outmost
                 relevance. In this paper we discuss the role of genetic
                 programming in predictive pharmacokinetics, considering
                 the estimation of adsorption, distribution, metabolism,
                 excretion and toxicity processes (ADMET) that a drug
                 undergoes into the patient's organism. We compare
                 genetic programming with other well known machine
                 learning techniques according to their ability to
                 predict oral bioavailability (%F), median oral lethal
                 dose (LD50) and plasma-protein binding levels (%PPB).
                 Since these parameters respectively characterise the
                 percentage of initial drug dose that effectively
                 reaches the systemic blood circulation, the harmful
                 effects and the distribution into the organism of a
                 drug, they are essential for the selection of
                 potentially good molecules. Our results suggest that
                 genetic programming is a valuable technique for
                 predicting pharmacokinetics parameters, both from the
                 point of view of the accuracy and of the generalisation
                 ability.",
  notes =        "GP, LS2-GP, LS2-C-GP, DF-GP, AIC, Weka ANN, SVM,
                 Linear regression",
}

Genetic Programming entries for Francesco Archetti Stefano Lanzeni Enza Messina Leonardo Vanneschi

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