Abstract
Oral bioavailability is the measurement of the fraction of admissible drug which reaches the site of action in unchanged form. It is one of the principal pharmacokinetic properties and can be predicted in an early phase of drug discovery and development process. Various computational methods have been used for predicting oral bioavailability of a drug candidate in the literature, which selects some of the compounds from the huge set which are most effective drug candidates and also reduces the cost factor of clinical trials. In this study, we have assigned a class label of all chemical compounds as high (Fractional Absorption F%\(\,\ge \,50\)) or low (Fractional Absorption F% < 50) oral bioavailability values. Here, the main aim is to obtain an effective model for classification of oral bioavailability data. In order to achieve this, we have preprocessed oral bioavailable data using Pearson correlation and subset selection as feature reduction methods and data discretization using binning. Discretization is one of the popular data preprocessing technique, which maps continuous data points into discrete values for easy data visualization and improves the performance of classification model. The effectiveness of feature reduction with discretization method for oral bioavailable data has been represented in terms of performance matrices like accuracy percentage, sensitivity, specificity, precision, and negative predictive value. Based on the comparative analysis of the performance of various classification models like artificial neural network (ANN), Bayesian classifier, support vector machine (SVM), K-nearest neighbor with feature-reduced discretized random forest model, we conclude that our proposed model gives better performance over the other compared models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Korkmaz, S., Zararsiz, G., Goksuluk, D.: MLViS: a web tool for machine learning-based virtual screening in early-phase of drug discovery and development. PloS One 10(4), e0124600 (2015)
Moda, T.L., Montanari, C.A., Andricopulo, A.D.: Hologram QSAR model for the prediction of human oral bioavailability. Bioorgan. Med. Chem. 15(24), 7738–7745 (2007)
Kumar, R., Sharma, A., Varadwaj, P.K.: A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine. J. Nat. Sci. Biol. Med. 2(2), 168 (2011)
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, 261523 (2002)
Waterbeemd, H., Gifford, E.: ADMET in silico modeling: towards prediction paradise? Nat. Rev. Drug Discov. 2, 192204 (2003)
Eisenberg, D.M., Davis, R.B., Ettner, S.L., Appel, S., Wilkey, S., et al.: Trends in alternative medicine use in the United States, 1990–1997: results of a follow-up national survey. JAMA 280, 1569–1575 (1998)
Sparreboom, A., Cox, M.C., Acharya, M.R., Figg, W.D.: Herbal remedies in the United States: potential adverse interactions with anticancer agents. J. Clin. Oncol. 22, 2489–2503 (2004)
Lipinski, C.A., et al.: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23(1–3), 3–25 (1997)
Lu, J.J., Crimin, K., Goodwin, J.T., Crivori, P., Orrenius, C., Xing, L., et al.: Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. J. Med. Chem. 47, 61047 (2004)
Somogyi, A., Eichelbaum, M., Gugler, R.: Prediction of bioavailability for drugs with a high first-pass effect using oral clearance data. Eur. J. Clin. Pharmacol. 22(1), 85–90 (1982)
Klopman, G., Stefan, L.R., Saiakhov, R.D.: ADME evaluation: 2. A computer model for the prediction of intestinal absorption in humans. Eur. J. Pharm. Sci. 17(4), 253–263 (2002)
Podlogar, B.L., Muegge, I., Brice, L.J.: Computational methods to estimate drug development parameters. Curr. Opin. Drug Discov. Dev. 4(1), 102–109 (2001)
Usansky, H.H., Sinko, P.J.: Estimating human drug oral absorption kinetics from Caco-2 permeability using an absorption-disposition model: model development and evaluation and derivation of analytical solutions for ka and Fa. J. Pharmacol. Exp. Ther. 314(1), 391–399 (2005)
Yoshida, F., Topliss, J.G.: QSAR model for drug human oral bioavailability 1. J. Med. Chem. 43(13), 2575–2585 (2000)
Archetti, F., et al.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genet. Program. Evolvable Mach. 8(4), 413–432 (2007)
Wang, J., et al.: Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion. J. Chem. Inf. Model. 46(6), 2674–2683 (2006)
Turner, J.V., Maddalena, D.J., Agatonovic-Kustrin, S.: Bioavailability prediction based on molecular structure for a diverse series of drugs. Pharm. Res. 21, 6882 (2004)
Frhlich, H., Sieker, F., Wegner, K., Zell, A.: Kernel functions for attributed molecular graphs—a new similarity based approach to ADME prediction in classification and regression. QSAR Comb. Sci. 25, 31726 (2005)
Liu, H.X., Hu, R.J., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D., et al.: The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine. J. Comput. Aided Mol. Des. 19, 3346 (2005)
Chen, B., Harrison, R.F., Papadatos, G., Willett, P., Wood, D.J., Lewell, X.Q., et al.: Evaluation of machine-learning methods for ligand-based virtual screening. J. Comp. Aid Mol. Des. 21, 5362 (2007)
Keiser, J., Manneck, T., Vargas, M.: Interactions of mefloquine with praziquantel in the Schistosoma mansoni mouse model and in vitro. J. Antimicrob. Chemother. 66, 17911797 (2011). https://doi.org/10.1093/jac/dkr178. PMID: 21602552
Bielska, E., et al.: Virtual screening strategies in drug design methods and applications. BioTechnol. J. Biotechnol. Comput. Biol. Bionanotechnol. 92(3) (2011)
Korkmaz, S., Zararsiz, G., Goksuluk, D.: Drug/nondrug classification using support vector machines with various feature selection strategies. Comput. Methods Programs Biomed. 117(2), 51–60 (2014)
Arulmozhi, V., Rajesh, R.: Chemoinformatics—a quick review. In: 2011 3rd International Conference on Electronics Computer Technology (ICECT), vol. 6. IEEE (2011)
Chonde, S., Kumara, S.: Cheminformatics: an introductory review. In: IIE Annual Conference, Proceedings. Institute of Industrial Engineers-Publisher (2014)
Hou, T., Wang, J., Zhang, W., Xu, X.: ADME evaluation in drug discovery. 6. If the oral bioavailability in human can be effectively predicted by simple molecular properties-based rules? J. Chem. Inf. Model. 47, 460–463 (2007)
Mitchell, J.B.O.: Machine learning methods in chemoinformatics. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4(5), 468–481 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shit, P., Banka, H. (2019). A Feature-Reduced Discretized Random Forest Model for Oral Bioavailability Data Classification. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-1135-2_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1134-5
Online ISBN: 978-981-13-1135-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)