Classification of Cytochrome P450 3A4 Ligands Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{gilbert:p450,
  author =       "Richard Gilbert and Kris Birchall and William Bains",
  title =        "Classification of Cytochrome P450 3A4 Ligands Using
                 Genetic Programming",
  year =         "2002",
  email =        "info@amedis-pharma.com",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://www.amedis-pharma.com/Docs/3A4_ligand_poster.ppt",
  abstract =     "The cytochrome P450 [CYP] family is a set of
                 haem-containing oxidoreductase enzymes which are
                 involved in the first-pass metabolism of xenobiotic
                 compounds such as drug molecules. CYP 3A4 is the most
                 abundant of these enzymes in humans, and is capable of
                 metabolising approximately 80percent of drugs to some
                 extent. As CYP3A4 has a limited capacity, both
                 competing substrates and inhibitors can affect the rate
                 at which CYP3A4 metabolises drugs, and hence the amount
                 of the compound that reaches systemic circulation.
                 Identifying whether a compound is metabolised by CYPs
                 in general, and CYP3A4 in particular, is important for
                 judging its potential as a drug. We describe an
                 approach to the computational identification of CYP3A4
                 ligands (substrates and inhibitors) that is based on a
                 type of evolutionary computing called genetic
                 programming. The method is a supervised learning
                 system, i.e. it is guided by past examples, in this
                 case actual measured biological data on CYP ligand
                 status. The GP system creates predictive models by
                 Darwinian operations of mutation, crossover and fitness
                 selection, operating on a population of potential
                 solutions. Parent solutions are chosen according to
                 their ability to explain the training data. New models
                 are generated by mutation or crossover, and may replace
                 less-fit individuals already in the population. After
                 sufficient iterations, the population comprises models
                 able to explain the observations much more effectively
                 than the initial random population. Applying this to
                 publicly available CYP3A4 data, we show that we can
                 predict the ligand status of a diverse set of known
                 drugs to approximately 90percent accuracy, and to
                 predict whether a ligand will be a substrate or an
                 inhibitor to approximately 85percent accuracy. The GP
                 method also identifies structural characteristics of
                 the molecule which it is using to build the decision
                 algorithms, and these are consistent with more
                 exhaustive, quantum mechanical predictions of substrate
                 status. The evolutionary nature of GPs allows
                 generation of multiple solutions, which allow
                 statistical validation of the results.",
  notes =        "Amedis Pharmaceuticals Limited, Upton House, Baldock
                 Street, Royston, Herts SG8 5AY, UK",
}

Genetic Programming entries for Richard J Gilbert Kris Birchall William Bains

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