Drug clearance pathway prediction using semi-supervised learning

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1 1,a) 1 1,2 802 Drug clearance pathway prediction using semi-supervised learning Yanagisawa Keisuke 1,a) Ishida Takashi 1 Akiyama Yutaka 1,2 Abstract: Nowadays, drug development requires too much time and budget, and it is necessary to reduce them. In order to accept a compound as a new drug, it must be confirmed that it is metabolized and excreted. In this respect, one of the computational methods used for selecting compounds is drug clearance pathway prediction. This prediction method uses well-known drug s clearance pathway data as a training set. However data is expensive to get, and thus there are too few data. For this reason, we evaluated the usefulness of semi-supervised learning in this prediction problem, and tried to improve accuracy of this clearance pathway prediction. We also tried to add some features of compounds which are selected from 802 features by greedy algorithm to improve accuracy and evaluated their effect. Keywords: drug clearance pathway prediction, drug discovery assistance, machine learning, semi-supervised learning [1] 1 Graduate School of Information Science and Engineering, Tokyo Institute of Technology 2 Education Academy of Computational Life Sciences, Tokyo Institute of technology a) yanagisawa@bi.cs.titech.ac.jp [2] 2 [3], [4] c 2014 Information Processing Society of Japan 1

2 UDP-glucuronosyl transferase, UGT [3] 3 P450 Cytochrome P450, CYP [4] 3 CYPRenal Organic anion transporting polypeptide, OATP 5 [5] Support Vector Machine, SVM [6] [7] [8] [6] 1 OATP f Table 1 Result of previous work Precision Recall (f ) CYP2C CYP2D CYP3A Renal OATP ZINC[9] DrugBank[10] Semi-supervised learning 1960 Scudder self-training[11] co-training[12] transductive [13] [14] Kusama [5] pread- MET v2.0 Bioinformatics & Molecular Design Research Center, South Korea {CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, Renal, OATP, UGT} Table 2 The number of positive data CYP1A2 18 CYP2C8 5 CYP2C9 17 CYP2C19 10 CYP2D6 25 CYP3A4 79 Renal 69 OATP 18 UGT ZINC[9] ZINC drug database subset c 2014 Information Processing Society of Japan 2

3 ZINC drug database subset 2.2 SVM Leaveone-out Cross Validation f f recall precision 3 Table 3 SVM Result using SVM (f ) CYP1A CYP2C CYP2C CYP2C CYP2D CYP3A Renal OATP UGT Average SVM 3 CYP2C19 UGT 2.3 transductive SVM transductive SVM[15] Joachim SVM Vapnik transductive [13] SVM transductive SVM ( 1 ) Support Vector SV ( 2 ) SV ( 3 ) SV ( 4 ) (3) ( 5 ) (3) (4) ( 6 ) SV SVM [16] SVM SVM RBF RBF γ C 2 15 γ 2 5 (1) 2 5 C 2 15 (2) γ 0, C 0 γ = {γ 0 2 3, γ ,, γ , γ } (3) C = {C 0 2 3, C ,, C , C } (4) = 576 transductive SVM SVMlight SVM SVMlight 2.4 c 2014 Information Processing Society of Japan 3

4 4 (1), (2), (3) 10 ( 1 ) ( 2 ) ( 3 ) ( 4 ) Leave-one-out Cross Validation f Table 4 4 Valuation of Semi-supervised learning (f ) (1) (2) (3) (4) CYP1A CYP2C CYP2C CYP2C CYP2D CYP3A Renal OATP UGT Average (1) (2), (3) 2.5 ZINC Table 5 5 Relationship between Unlabeled-data and precision (f ) CYP1A CYP2C CYP2C CYP2C CYP2D CYP3A Renal OATP UGT Average / 3.1 Canvas v Schrödinger, USA Canvas 757 [17] Protein Data Bank PDB [18] 15 CYP c 2014 Information Processing Society of Japan 4

5 AutoDock[19], Glide(SP XP )[20] [21] ( 1 ) = 1604 f ( 2 ) f (1) ( 3 ) f LIBSVM[22] LIBSVM transductive SVM 6 Table 6 6 The result of feature selection f f CYP1A CYP2C CYP2C CYP2C CYP2D CYP3A Renal OATP UGT Average f UGT Table 7 7 UGT Semi-supervised learning with additional feature (f ) , 200, f 10 1, , transductive SVM SVM ZINC c 2014 Information Processing Society of Japan 5

6 1 transductive SVM transductive SVM [1] PhRMA New Drug Approvals in 2011, [2],, :,,, pp.593-pp.611, 2005 [3] Sorich M.J., Miners J.O., McKinnon R.A. et al.: Comparison of Linear and Nonlinear Classification Algorithms for Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms, Journal of Chemical Information and Computer Sciences 43(6), pp.2019-pp.2024, 2003 [4] Hammann F., Gutmann H., Baumann U. et al.: Classification of Cytochrome P450 Activities Using Machine Learning Methods, Molecular Pharmaceutics 6(6), pp.1920-pp.1926, 2009 [5] Kusama M., Toshimoto K., Maeda K. et al.: In silico classification of major clearance pathways of drugs with their physiochemical parameters., Drug Metabolism and Disposition 38(8), pp.1362-pp.1370, 2010 [6],, et al.: SVM - -, 2010-BIO-20(8), pp.1-pp.8, 2009 [7],, et al.:, 2009-BIO-17(10), pp.1-pp.8, 2009 [8],, et al.:, 2010-BIO-21(20), pp.1-pp.8, 2010 [9] John J.I., Teague S., Michael M.M. et al.: ZINC: A Free Tool to Discover Chemistry for Biology., Journal of Chemical Information and Modeling 52(7), pp pp.1768, 2012 [10] Law V., Knox C., Djoumbou Y. et al.: DrugBank 4.0: shedding new light on drug metabolism, Nucleic acids research, 42(D1), D1091-D1097, 2014 [11] Scudder III H.: Probability of error of some adaptive pattern-recognition machines., Information Theory, IEEE Transactions 11(3), pp.363-pp.371, 1965 [12] Blum A., Mitchell T.: Combining labeled and unlabeled data with co-training., Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp.92-pp.100, 1998 [13] Vapnik V.: Statistical Learning Theory., Wiley, [14] Röttig M., Medema M.H., Blin K. et al.: NRPSpredictor2 - a web server for predicting NRPS adenylation domain specificity, Nucleic Acids Research 39(suppl 2), pp.362-pp.367, 2011 [15] Joachims T.: Transductive inference for text classification using support vector machines, Proceedings of the Sixteenth International Conference on Machine Learning, pp.200-pp.209, 1999 [16] Collobert R., Sinz F., Weston J. et al.: Large scale transductive SVMs, The Journal of Machine Learning Research 7, pp.1687-pp.1712, 2006 [17],, et al.:, 2013-BIO-34(15), pp.1-pp.6, 2013 [18] Berman H.M., Westbrook J., Feng Z. et al.: The Protein Data Bank, Nucleic Acids Research 28(1), pp.235- pp.242, 2000 [19] Morris G.M., Huey R., Lindstrom W. et al.: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility., Journal of Computational Chemistry 30(16), pp.2785-pp.2791, 2009 [20] Friesner R.A., Banks J.L., Murphy R.B. et al.: Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy., Journal of Medicinal Chemistry 47(7), pp.1739-pp.1749, 2004 [21] Caruana R., Freitag D.: Greedy attribute selection, Proceedings of the Eleventh International Conference on Machine Learning, pp.28-pp.36, 1994 [22] Cheng C.c., Lin C.J.: LIBSVM: a libbrary for support vector machines., ACM Transactions on Intelligent Systems and Technology 2(3), pp.27:1-pp27:27, 2011 c 2014 Information Processing Society of Japan 6