Abstract
Computational methods allowing reliable pharmacokinetics predictions for newly synthesized compounds are critically relevant for drug discovery and development. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterize the harmful effects and the distribution into human body of a drug, their accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting pharmacokinetics parameters, both from the point of view of the accurateness and of the generalization ability.
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References
Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for human oral bioavailability of drugs. In: M. Cattolico (ed.) Proceedings of the 8th annual conference on Genetic and Evolutionary Computation, Seattle, Washington, USA, pp. 255–262 (2006)
Colmenarejo, G., Alvarez-Pedraglio, A., Lavandera, J.L.: Chemoinformatic models to predict binding affinities to human serum albumin. Journal of Medicinal Chemistry 44, 4370–4378 (2001)
Akaike, H.: Information theory and an extension of maximum likelihood principle. In: 2nd International Symposium on Information Theory, Akademia Kiado (June 1973)
van de Waterbeemd, H., Gifford, E.: ADMET in silico modeling: towards prediction paradise? Nature Reviews Drug Discovery 2, 192–204 (2003)
Van de Waterbeemd, H., Rose, S.: In: Wermuth, L.G. (ed.) The Practice of Medicinal Chemistry, 2nd edn., pp. 1367–1385. Academic Press, San Diego (2003)
Van de Waterbeemd, H., Smith, D.A., Jones, B.C.: Lipophilicity in PK design: methyl, ethyl, futile. Journal of Computationally aided Molecular Design 15, 273–286 (2001)
Kola, I., Landis, J.: Can the pharmaceutical industry reduce attrition rates? Nature Reviews Dug Discovery 3, 711–716 (2004)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer series in statistics. Springer, Heidelberg (1999)
Feng, J., Lurati, L., Ouyang, H., Robinson, T., Wang, Y., Yuan, S., Young, S.S.: Predictive toxicology: benchmarking molecular descriptors and statistical methods. Journal of Chemical Information Computer Science 43, 1463–1470 (2003)
Zupan, J., Gasteiger, P.: Neural Networks in chemistry and drug design: an introduction, 2nd edn. Wiley, Chichester (1999)
Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 71–83. Springer, Heidelberg (2003)
Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)
Berezhkovskiy, L.M.: Determination of Drug Binding to Plasma Proteins Using Competitive Equilibrium Binding to Dextran-Coated Charcoal. Journal of Pharmacokinetics and Pharmacodynamics 33(5), 920–937 (2006)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning. PhD thesis, Department of Computer Science, Waikato University, Hamilton, NZ (1998)
Nicolotti, O., Gillet, V.J., Fleming, P.J., Green, D.V.: Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. Journal Med. Chem. 45(23), 5069–5080 (2002)
Rousseeuw, P.J., Leroy, A.M.: Robust regression and outlier detection. Wiley, New York (1987)
Todeschini, R., Consonni, V.: Handbook of Molecular Descriptors. Wiley-VCH, Weinheim (2000)
REACH. Registration, Evaluation and Authorisation of Chemicals (2006), http://ec.europa.eu/environment/chemicals/reach/reach_intro.htm
David, S., Wishart, C., Knox, A.C., Guo, S., Shrivastava, M., Hassanali, P., Stothard, Z., Chang, Z., Woolsey J.: DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34 (2006), doi:10.1093/nar/gkj067
Haykin, S.: Neural Networks: a comprehensive foundation. Prentice Hall, London (1999)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Technical Report, Technical Report Series - NC2-TR-1998-030, NeuroCOLT2 (1999)
Kennedy, T.: Managing the drug discovery/development interface. Drug Discovery Today 2, 436–444 (1997)
Martin, T.M., Young, D.M.: Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds to the Fathead Minnow (Pimephales promelas) Using a Group Contribution Method. Chemical Research in Toxicology 14(10), 1378–1385 (2001)
Tetko, I.V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V.A., Radchenko, E.V., Zefirov, N.S., Makarenko, A.S., Tanchuk, V.Y., Prokopenko, V.V.: Virtual computational chemistry laboratory - design and description. Journal of Computer Aided Molecular Design 19, 453–463 (2005), see www.vcclab.org
Norinder, U., Bergstrom, C.A.S.: Prediction of ADMET properties. ChemMedChem 1, 920–937 (2006)
Venkatraman, V., Dalby, A.R., Yang, Z.R.: Evaluation of mutual information and genetic programming for feature selection in QSAR. Journal Chem. Inf. Comput. Sci. 44(5), 1686–1692 (2004)
Langdon, W. B., Barrett, S. J.: Genetic Programming in data mining for drug discovery. In: Evolutionary computing in data mining, pp. 211–235 (2004)
Weka: a multi-task machine learning software developed by Waikato University, see (2006), http://www.cs.waikato.ac.nz/ml/weka/
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Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L. (2007). Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_2
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DOI: https://doi.org/10.1007/978-3-540-71783-6_2
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