Genetic programming for QSAR investigation of docking energy

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  title =        "Genetic programming for QSAR investigation of docking
  author =       "Francesco Archetti and Ilaria Giordani and 
                 Leonardo Vanneschi",
  journal =      "Applied Soft Computing",
  volume =       "10",
  number =       "1",
  pages =        "170--182",
  year =         "2010",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Regression, Docking energy, Computational
                 biology, Drug design, QSAR",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2009.06.013",
  URL =          "",
  abstract =     "Statistical methods, and in particular Machine
                 Learning, have been increasingly used in the drug
                 development workflow to accelerate the discovery phase
                 and to eliminate possible failures early during
                 clinical developments. In the past, the authors of this
                 paper have been working specifically on two problems:
                 (i) prediction of drug induced toxicity and (ii)
                 evaluation of the target drug chemical interaction
                 based on chemical descriptors. Among the numerous
                 existing Machine Learning methods and their application
                 to drug development (see for instance [F. Yoshida, J.G.
                 Topliss, QSAR model for drug human oral
                 bioavailability, Journal of Medicinal Chemistry 43
                 (2000) 2575-2585; Frohlich, J. Wegner, F. Sieker, A.
                 Zell, Kernel functions for attributed molecular graphs
                 - a new similarity based approach to ADME prediction in
                 classification and regression, QSAR and Combinatorial
                 Science, 38(4) (2003) 427-431; C.W. Andrews, L.
                 Bennett, L.X. Yu, Predicting human oral bioavailability
                 of a compound: development of a novel quantitative
                 structure-bioavailability relationship, Pharmacological
                 Research 17 (2000) 639-644; J Feng, L. Lurati, H.
                 Ouyang, T. Robinson, Y. Wang, S. Yuan, S.S. Young,
                 Predictive toxicology: benchmarking molecular
                 descriptors and statistical methods, Journal of
                 Chemical Information Computer Science 43 (2003)
                 1463-1470; T.M. Martin, D.M. Young, Prediction of the
                 acute toxicity (96-h LC50) of organic compounds to the
                 fat head minnow (Pimephales promelas) using a group
                 contribution method, Chemical Research in Toxicology
                 14(10) (2001) 1378-1385; G. Colmenarejo, A.
                 Alvarez-Pedraglio, J.L. Lavandera, Chemoinformatic
                 models to predict binding affinities to human serum
                 albumin, Journal of Medicinal Chemistry 44 (2001)
                 4370-4378; J. Zupan, P. Gasteiger, Neural Networks in
                 Chemistry and Drug Design: An Introduction, 2nd
                 edition, Wiley, 1999]), we have been specifically
                 concerned with Genetic Programming. A first paper [F.
                 Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic
                 programming for computational pharmacokinetics in drug
                 discovery and development, Genetic Programming and
                 Evolvable Machines 8(4) (2007) 17-26
                 \cite{Archetti:2007:GPEM}] has been devoted to problem
                 (i). The present contribution aims at developing a
                 Genetic Programming based framework on which to build
                 specific strategies which are then shown to be a
                 valuable tool for problem (ii). In this paper, we use
                 target estrogen receptor molecules and genistein based
                 drug compounds. Being able to precisely and efficiently
                 predict their mutual interaction energy is a very
                 important task: for example, it may have an immediate
                 relationship with the efficacy of genistein based drugs
                 in menopause therapy and also as a natural prevention
                 of some tumours. We compare the experimental results
                 obtained by Genetic Programming with the ones of a set
                 of non-evolutionary Machine Learning methods, including
                 Support Vector Machines, Artificial Neural Networks,
                 Linear and Least Square Regression. Experimental
                 results confirm that Genetic Programming is a promising
                 technique from the viewpoint of the accuracy of the
                 proposed solutions, of the generalization ability and
                 of the correlation between predicted data and correct

Genetic Programming entries for Francesco Archetti Ilaria Giordani Leonardo Vanneschi