Abstract
The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current ”target-rich, lead-poor” scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using 1D and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use 1D molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Plewczynski, D.: Tvscreen: Trend vector virtual screening of large commercial compounds collections. In: International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, BIOTECHNO 2008, pp. 59–63 (2008)
Graham, J., Page, C., Kamal, A.: Accelerating the drug design process through parallel inductive logic programming data mining. In: Computational Systems Bioinformatics Conference, p. 400. International IEEE Computer Society, Los Alamitos (2003)
Barrett, S.J., Langdon, W.B.: Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development. In: Tiwari, A., Knowles, J., Avineri, E., Dahal, K., Roy, R. (eds.) Applications of Soft Computing: Recent Trends. Advances in Soft Computing, pp. 99–110. Springer, Heidelberg (2006)
Duch, W., Swaminathan, K., Meller, J.: Artificial intelligence approaches for rational drug design and discovery. Current Pharmaceutical Design 13, 1497–1508 (2007)
van de Waterbeemd, H., Gifford, E.: Admet in silico modelling: towards prediction paradise? Nat. Rev. Drug. Discov. 2(3), 192–204 (2003)
Neagu, D., Craciun, M., Stroia, S., Bumbaru, S.: Hybrid intelligent systems for predictive toxicology - a distributed approach. In: International Conference on Intelligent Systems Design and Applications, pp. 26–31 (2005)
Hansch, C., Maloney, P., Fujita, T., Muir, R.: Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature 194, 178–180 (1962)
White, A., Mueller, R., Gallavan, R., Aaron, S., Wilson, A.: A multiple in silico program approach for the prediction of mutagenicity from chemical structure. Mutation Research/Genetic Toxicology and Environmental Mutagenesis 539, 77–89 (2003)
Richard, A.: Future of toxicology-predictive toxicology: An expanded view of “chemical toxicity”. Chem. Res. Toxicol. 19(10), 1257–1262 (2006)
Amini, A., Muggleton, S., Lodhi, H., Sternberg, M.: A novel logic-based approach for quantitative toxicology prediction. J. Chem. Inf. Model. 47(3), 998–1006 (2007)
Dearden, J.: In silico prediction of drug toxicity. Journal of computer-aided molecular design 17(2-4), 119–127 (2003)
Ekins, S.: Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals. Wiley Series on Technologies for the Pharmaceutical Industry. Wiley-Interscience, Hoboken (2007)
Kazius, J., Mcguire, R., Bursi, R.: Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem. 48(1), 312–320 (2005)
Russom, C., Bradbury, S., Broderius, S., Hammermeister, D., Drummond, R.: Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (pimephales promelas). Environmental toxicology and chemistry 16(5), 948–967 (1997)
Richard, A., Williams, C.: Distributed structure-searchable toxicity (dsstox) public database network: a proposal. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 499, 27–52 (2002)
Gold, L., Manley, N., Slone, T., Ward, J.: Compendium of chemical carcinogens by target organ: Results of chronic bioassays in rats, mice, hamsters, dogs, and monkeys. Toxicologic Pathology 29(6), 639–652 (2001)
Fang, H., Tong, W., Shi, L., Blair, R., Perkins, R., Branham, W., Hass, B., Xie, Q., Dial, S., Moland, C., Sheehan, D.: Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. Chem. Res. Toxicol. (14), 280–294 (2001)
Woo, Y., Lai, D., McLain, J., Manibusan, M., Dellarco, V.: Use of mechanism-based structure-activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products. Environ. Health Perspect (110), 75–87 (2002)
Todeschini, R., Consonni, V., Mannhold, R., Kubinyi, H., Timmerman, H.: Handbook of Molecular Descriptors. Wiley-VCH, Chichester (2000)
Guha, R., Howard, M., Hutchison, G., Murray-Rust, P., Rzepa, H., Steinbeck, C., Wegner, J., Willighagen, E.: The blue obelisk – interoperability in chemical informatics. J. Chem. Inf. Model. 3(46), 991–998 (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Bahler, D., Stone, B., Wellington, C., Bristol, D.: Symbolic, neural, and bayesian machine learning models for predicting carcinogenicity of chemical compounds. J. Chemical Information and Computer Sciences 8, 906–914 (2000)
Ivanciuc, O.: Aquatic toxicity prediction for polar and nonpolar narcotic pollutants with support vector machines. Internet Electronic Journal of Molecular Design (2), 195–208 (2003)
Ivanciuc, O.: Weka machine learning for predicting the phospholipidosis inducing potential. Current Topics in Medicinal Chemistry (8) (2008)
Pugazhenthi, D., Rajagopalan, S.: Machine learning technique approaches in drug discovery, design and development. Information Technology Journal 5(6), 718–724 (2007)
Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L., Pähler, A.: Computational toxicology in drug development. Drug Discovery Today 8(7) (2008)
Judson, R., Elloumi, F., Setzer, R., Li, Z., Shah, I.: A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformatics (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pereira, M., Costa, V.S., Camacho, R., Fonseca, N.A., Simões, C., Brito, R.M.M. (2009). Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases. In: Guimarães, K.S., Panchenko, A., Przytycka, T.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2009. Lecture Notes in Computer Science(), vol 5676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03223-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-03223-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03222-6
Online ISBN: 978-3-642-03223-3
eBook Packages: Computer ScienceComputer Science (R0)