Abstract
Compounds in drug screening-libraries should resemble pharmaceuticals. To operationally test this, we analysed the compounds in terms of known drug-like filters and developed a novel machine learning method to discriminate approved pharmaceuticals from “drug-like” compounds. This method uses both structural features and molecular properties for discrimination. The method has an estimated accuracy of 91% in discriminating between the Maybridge HitFinder library and approved pharmaceuticals, and 99% between the NATDiverse collection (from Analyticon Discovery) and approved pharmaceuticals. These results show that Lipinski’s Rule of 5 for oral absorption is not sufficient to describe “drug-likeness” and be the main basis of screening-library design.
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References
Leach, A.R., Gillet, V.J.: An Introduction to Chemoinformatics. Kluwer Academic Publishers, Dordrecht (2003)
Hann, M.M., Leach, A.R., Harper, G.: Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. Journal of Chemical Information and Computer Sciences 41(3), 856–864 (2001)
Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J.: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 23(1-3), 3–25 (1997)
Ajay, W., Walters, W.P., Murcko, M.A.: Can We Learn To Distinguish between "Drug-like" and "Nondrug-like" Molecules? J. Med. Chem. 41(18), 3314–3324 (1998)
Sadowski, J., Kubinyi, H.: A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329 (1998)
Murcia-Soler, M., Pérez-Giménez, F., García-March, F.J., Salabert-Salvador, M.T., Díaz-Villanueva, W., Castro-Bleda, M.J.: Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks. J. Chem. Inf. Comput. Sci. 43(5), 1688–1702 (2003)
Wagener, M., van Geerestein, V.J.: Potential drugs and nondrugs: prediction and identification of important structural features. J. Chem. Inf. Comput. Sci. 40 (2000)
Oprea, T.I., Davis, A.M., Teague, S.J., Leeson, P.D.: Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315 (2001)
Oprea, T.I.: Lead structure searching: Are we looking at the appropriate property? J. Comput.-Aided Mol. Design 16, 325–334 (2002)
Veber, D.F., Johnson, S.R., Cheng, H.-Y., Smith, B.R., Ward, K.W., Kopple, K.D.: Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002)
Baurin, N., Baker, R., Richardson, C.M., Chen, I.-J., Foloppe, N., Potter, A., Jordan, A., Roughley, S., Parratt, M.J., Greaney, P., Morley, D., Hubbard, R.E.: Drug-like Annotation and Duplicate Analysis of a 23-Supplier Chemical Database Totalling 2.7 Million Compounds. Journal of Chemical Information and Modeling 44(2), 643–651 (2004)
King, R.D., Muggleton, S.H., Srinivasan, A., Sternberg, M.J.E.: Structure activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity using inductive logic programming. Proceedings of the National Academy of Sciences, USA 93, 438–442 (1996)
Buttingsrud, B., Ryeng, E., King, R.D., Alsberg, B.K.: Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships. Journal of Computer-Aided Molecular Design 20(6), 361–373 (2006)
Bader, R.F.W.: Atoms in Molecules - A Quantum Theory. Oxford University Press, Oxford (1990)
Liu, K., Feng, J., Young, S.S.: PowerMV: A Software Environment for Molecular Viewing, Descriptor Generation, Data Analysis and Hit Evaluation. J. Chem. Inf. Model. 45, 515–522 (2005)
Guha, R., Howard, M.T., Hutchison, G.R., Murray-Rust, P., Rzepa, H., Steinbeck, C., Wegner, J.K., Willighagen, E.: The Blue Obelisk – Interoperability in Chemical Informatics. J. Chem. Inf. Model. 46(3), 991–998 (2006)
Codd, E.F.: Recent Investigations into Relational Data Base Systems. IBM Research Report RJ1385 (April 23, 1974); republished in Proc. 1974 Congress, Stockholm, Sweden. North-Holland, New York (1974)
Blockeel, H., De Raedt, L.: Top-down induction of first order logical decision trees. Artificial Intelligence 101(1-2), 285–297 (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann series in Machine Learning. Morgan Kaufmann, San Francisco (1993)
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Schierz, A.C., King, R.D. (2009). Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds. In: Kadirkamanathan, V., Sanguinetti, G., Girolami, M., Niranjan, M., Noirel, J. (eds) Pattern Recognition in Bioinformatics. PRIB 2009. Lecture Notes in Computer Science(), vol 5780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04031-3_29
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DOI: https://doi.org/10.1007/978-3-642-04031-3_29
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