N. Aronszajn, Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, issue.3, pp.337-404, 1950.
DOI : 10.1090/S0002-9947-1950-0051437-7

T. K. Attwood, P. Bradley, D. R. Flower, A. Gaulton, N. Maudling et al., PRINTS and its automatic supplement, prePRINTS, Nucleic Acids Research, vol.31, issue.1, pp.31-400, 2003.
DOI : 10.1093/nar/gkg030

C. Azencott, A. Ksikes, S. J. Swamidass, J. H. Chen, L. Ralaivola et al., One-to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties, J Chem Inf Model, issue.3, pp.47-965, 2007.

K. V. Balakin, S. E. Tkachenko, S. A. Lang, I. Okun, A. A. Ivashchenko et al., Property-based design of gpcr-targeted library, J Chem Inf Comput Sci, issue.6, pp.42-1332, 2002.

J. Ballesteros and K. Palczewski, G protein-coupled receptor drug discovery: implications from the crystal structure of rhodopsin, Curr Opin Drug Discov Devel, vol.4, issue.5, pp.561-574, 2001.

A. Ben-hur and D. Brutlag, Remote homology detection: a motif based approach, Bioinformatics, vol.19, issue.Suppl 1, pp.26-33, 2003.
DOI : 10.1093/bioinformatics/btg1002

J. R. Bock and D. A. Gough, Virtual Screen for Ligands of Orphan G Protein-Coupled Receptors, Journal of Chemical Information and Modeling, vol.45, issue.5, pp.1402-1414, 2005.
DOI : 10.1021/ci050006d

K. Borgwardt, C. Ong, S. Schönauer, S. Vishwanathan, A. Smola et al., Protein function prediction via graph kernels, Bioinformatics, vol.21, issue.Suppl 1, pp.47-56, 2005.
DOI : 10.1093/bioinformatics/bti1007

K. M. Borgwardt and H. Kriegel, Shortest-path kernels on graphs. Int, Conf on Data Mining, pp.74-81, 2005.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

D. Butina, M. D. Segall, and K. Frankcombe, Predicting ADME properties in silico: methods and models, Drug Discovery Today, vol.7, issue.11, pp.83-88, 2002.
DOI : 10.1016/S1359-6446(02)02288-2

E. Byvatov, U. Fechner, J. Sadowski, and G. Schneider, Comparison of support vector machine and artificial neural network systems for drug/nondrug classification, J Chem Inf Comput Sci, issue.6, pp.43-1882, 2003.

M. Cuturi and J. Vert, The context-tree kernel for strings, Neural Networks, vol.18, issue.8, pp.1111-1123, 2005.
DOI : 10.1016/j.neunet.2005.07.010
URL : https://hal.archives-ouvertes.fr/hal-00433583

P. Dobson and A. Doig, Predicting Enzyme Class From Protein Structure Without Alignments, Journal of Molecular Biology, vol.345, issue.1, pp.187-199, 2005.
DOI : 10.1016/j.jmb.2004.10.024

T. Evgeniou, C. Micchelli, and M. Pontil, Learning multiple tasks with kernel methods, J. Mach. Learn. Res, vol.6, pp.615-637, 2005.

T. Gärtner, P. Flach, and S. Wrobel, On Graph Kernels: Hardness Results and Efficient Alternatives, Proceedings of the Sixteenth Annual Conference on Computational Learning Theory and the Seventh Annual Workshop on Kernel Machines, volume 2777 of Lecture Notes in Computer Science, pp.129-143, 2003.
DOI : 10.1007/978-3-540-45167-9_11

J. Gasteiger and T. Engel, Chemoinformatics : a Textbook, 2003.
DOI : 10.1002/3527601643

U. Gether, Uncovering Molecular Mechanisms Involved in Activation of G Protein-Coupled Receptors, Endocrine Reviews, vol.21, issue.1, pp.90-113, 2000.
DOI : 10.1210/edrv.21.1.0390

I. Halperin, B. Ma, H. Wolfson, and R. Nussinov, Principles of docking: An overview of search algorithms and a guide to scoring functions, Proteins: Structure, Function, and Genetics, vol.43, issue.8, pp.409-443, 2002.
DOI : 10.1002/prot.10115

A. L. Hopkins and C. R. Groom, The druggable genome, Nature Reviews Drug Discovery, vol.180, issue.9, pp.727-730, 2002.
DOI : 10.1126/science.287.5461.2204

T. Horváth, T. Gärtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.158-167, 2004.
DOI : 10.1145/1014052.1014072

O. Ivanciuc, Applications of Support Vector Machines in Chemistry, Reviews in Computational Chemistry, pp.291-400, 2007.
DOI : 10.1002/9780470116449.ch6

T. Jaakkola, M. Diekhans, and D. Haussler, A Discriminative Framework for Detecting Remote Protein Homologies, Journal of Computational Biology, vol.7, issue.1-2, pp.95-114, 2000.
DOI : 10.1089/10665270050081405

S. E. Jaroch and H. Weinmann, Chemical Genomics: Small Molecule Probes to Study Cellular Function. Ernst Schering Research Foundation Workshop, 2006.
DOI : 10.1007/978-3-540-37635-4

M. Kanehisa, S. Goto, S. Kawashima, and A. Nakaya, The KEGG databases at GenomeNet, Nucleic Acids Research, vol.30, issue.1, pp.42-46, 2002.
DOI : 10.1093/nar/30.1.42

M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, The KEGG resource for deciphering the genome, Nucleic Acids Research, vol.32, issue.90001, pp.277-80, 2004.
DOI : 10.1093/nar/gkh063

H. Kashima, K. Tsuda, and A. Inokuchi, Marginalized Kernels between Labeled Graphs, Proceedings of the Twentieth International Conference on Machine Learning, pp.321-328, 2003.

H. Kashima, K. Tsuda, and A. Inokuchi, Kernels for graphs, Kernel Methods in Computational Biology, pp.155-170, 2004.

T. Klabunde, Chemogenomics Approaches to Ligand Design, Ligand Design for G Protein-coupled Receptors, chapter 7, pp.115-135, 2006.
DOI : 10.1002/3527608249.ch7

T. Klabunde, Chemogenomic approaches to drug discovery: similar receptors bind similar ligands, British Journal of Pharmacology, vol.47, issue.1, 2007.
DOI : 10.1038/sj.bjp.0707308

A. Hlemann, R. H. Porter, M. Stahl, and P. R. Gerber, An automated system for the analysis of g protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application, J Chem Inf Model, issue.5, pp.45-1324, 2005.

R. Kuang, E. Ie, K. Wang, K. Wang, M. Siddiqi et al., PROFILE-BASED STRING KERNELS FOR REMOTE HOMOLOGY DETECTION AND MOTIF EXTRACTION, Journal of Bioinformatics and Computational Biology, vol.03, issue.03, pp.527-550, 2005.
DOI : 10.1142/S021972000500120X

H. Kubinyi, G. Müller, R. Mannhold, and G. Folkers, Chemogenomics in Drug Discovery: A Medicinal Chemistry Perspective. Methods and Principles in Medicinal Chemistry, 2004.

G. R. Lanckriet, T. De-bie, N. Cristianini, M. I. Jordan, N. et al., A statistical framework for genomic data fusion, Bioinformatics, vol.20, issue.16, pp.20-2626, 2004.
DOI : 10.1093/bioinformatics/bth294

C. Leslie, E. Eskin, N. , and W. , THE SPECTRUM KERNEL: A STRING KERNEL FOR SVM PROTEIN CLASSIFICATION, Biocomputing 2002, pp.564-575, 2002.
DOI : 10.1142/9789812799623_0053

C. S. Leslie, E. Eskin, A. Cohen, J. Weston, N. et al., Mismatch string kernels for discriminative protein classification, Bioinformatics, vol.20, issue.4, pp.467-476, 2004.
DOI : 10.1093/bioinformatics/btg431

P. Mahé and J. Vert, Graph kernels based on tree patterns for molecules, Machine Learning, vol.21, issue.Suppl.??1, 2006.
DOI : 10.1007/s10994-008-5086-2

P. Mahé, N. Ueda, T. Akutsu, J. Perret, and J. Vert, Graph Kernels for Molecular Structure???Activity Relationship Analysis with Support Vector Machines, Journal of Chemical Information and Modeling, vol.45, issue.4, pp.939-51, 2005.
DOI : 10.1021/ci050039t

P. Mahé, L. Ralaivola, V. Stoven, and J. Vert, The Pharmacophore Kernel for Virtual Screening with Support Vector Machines, Journal of Chemical Information and Modeling, vol.46, issue.5, pp.2003-2014, 2006.
DOI : 10.1021/ci060138m

C. Manly, S. Louise-may, and J. Hammer, The impact of informatics and computational chemistry on synthesis and screening, Drug Discovery Today, vol.6, issue.21, pp.1101-1110, 2001.
DOI : 10.1016/S1359-6446(01)01990-0

S. Oloff, S. Zhang, N. Sukumar, C. Breneman, and A. Tropsha, Chemometric Analysis of Ligand Receptor Complementarity:?? Identifying Complementary Ligands Based on Receptor Information (CoLiBRI), Journal of Chemical Information and Modeling, vol.46, issue.2, pp.844-851, 2006.
DOI : 10.1021/ci050065r

J. Qiu, M. Hue, A. Ben-hur, J. Vert, N. et al., A structural alignment kernel for protein structures, Bioinformatics, vol.23, issue.9, pp.1090-1098, 2007.
DOI : 10.1093/bioinformatics/btl642
URL : https://hal.archives-ouvertes.fr/hal-00433578

L. Ralaivola, S. J. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural Networks, vol.18, issue.8, pp.1093-1110, 2005.
DOI : 10.1016/j.neunet.2005.07.009

J. Ramon and T. Gärtner, Expressivity versus efficiency of graph kernels, Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences, pp.65-74, 2003.

D. Rognan, Chemogenomic approaches to rational drug design, British Journal of Pharmacology, vol.62, issue.1, 2007.
DOI : 10.1038/sj.bjp.0707307
URL : https://hal.archives-ouvertes.fr/hal-00195211

B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2002.

B. Schölkopf, K. Tsuda, and J. Vert, Kernel Methods in Computational Biology, 2004.

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, 2002.
DOI : 10.1002/9783527613106

K. Tsuda, T. Kin, and K. Asai, Marginalized kernels for biological sequences, Bioinformatics, vol.18, issue.Suppl 1, pp.268-275, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S268

V. N. Vapnik, Statistical Learning Theory, 1998.

J. Vert, A tree kernel to analyse phylogenetic profiles, Bioinformatics, vol.18, issue.Suppl 1, pp.276-284, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S276
URL : https://hal.archives-ouvertes.fr/hal-00433591

J. Vert, H. Saigo, and T. Akutsu, Local alignment kernels for biological sequences, Kernel Methods in Computational Biology, pp.131-154, 2004.

V. V. Zernov, K. V. Balakin, A. A. Ivaschenko, N. P. Savchuk, and I. V. Pletnev, Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions, J Chem Inf Comput Sci, issue.6, pp.43-2048, 2003.