Predicting Biochemical Interactions -- Human P450 2D6 Enzyme Inhibition

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{langdon:2003:CEC,
  author =       "W. B. Langdon and S. J. Barrett and B. F. Buxton",
  title =        "Predicting Biochemical Interactions -- Human {P450}
                 2{D6} Enzyme Inhibition",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "807--814",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, QSAR, drug,
                 P450, Pareto multi-objective fitness",
  ISBN =         "0-7803-7804-0",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2003.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2003.ps.gz",
  doi =          "doi:10.1109/CEC.2003.1299750",
  size =         "8 pages",
  abstract =     "In silico screening of chemical libraries or virtual
                 chemicals may reduce drug discovery and medicine
                 optimisation lead times and increase the probability of
                 success by directing search through chemical space.
                 About a dozen intelligent pharmaceutical QSAR modelling
                 techniques were used to predict IC50 concentration
                 (three classes) of drug interaction with a cell wall
                 enzyme (P450 CYC2D6). Genetic programming gave
                 comprehensible cheminformatics models which generalised
                 best. This was shown by a blind test on GlaxoWelcome
                 molecules of machine learning knowledge nuggets mined
                 from SmithKline Beecham compounds. Performance on
                 similar chemicals (interpolation) and diverse chemicals
                 (extrapolation) suggest generalisation is more
                 difficult than avoiding over fitting.

                 Two GP approaches, classification via regression using
                 a multi-objective fitness measure and a direct winner
                 takes all (WTA) or one versus all (OVA) classification,
                 are described. Predictive rules were compressed by
                 separate follow up GP runs seeded with the best
                 program.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",
}

Genetic Programming entries for William B Langdon S J Barrett Bernard Buxton