A. Joseph, R. W. Dimasi, H. Hansen, and . Grabowski, The price of innovation: new estimates of drug development costs, Journal of health economics, vol.22, issue.2, pp.151-185, 2003.

L. Andrew and . Hopkins, Drug discovery: predicting promiscuity, Nature, vol.462, issue.7270, p.167, 2009.

C. Azencott, The drug discovery pipeline, 2018.

, Drug discovery: a historical perspective, Jürgen Drews, vol.287, issue.5460, pp.1960-1964, 2000.

H. Konrad, H. Bleicher, K. Böhm, A. I. Müller, and . Alanine, A guide to drug discovery: hit and lead generation: beyond high-throughput screening, Nature reviews Drug discovery, vol.2, issue.5, p.369, 2003.

S. Regine, C. Bohacek, W. C. Mcmartin, and . Guida, The art and practice of structure-based drug design: a molecular modeling perspective, Medicinal research reviews, vol.16, issue.1, pp.3-50, 1996.

D. Robert, Y. Brown, and . Martin, The information content of 2d and 3d structural descriptors relevant to ligand-receptor binding, Journal of Chemical Information and Computer Sciences, vol.37, issue.1, pp.1-9, 1997.

J. Gasteiger and T. Engel, Chemoinformatics: a textbook, 2006.

E. Evan, Y. Bolton, . Wang, S. Paul-a-thiessen, and . Bryant, Pubchem: integrated platform of small molecules and biological activities, Annual reports in computational chemistry, vol.4, pp.217-241, 2008.

A. Gaulton, J. Louisa, P. Bellis, J. Bento, M. Chambers et al., Chembl: a large-scale bioactivity database for drug discovery, Nucleic acids research, vol.40, issue.D1, pp.1100-1107, 2012.

V. Law, C. Knox, Y. Djoumbou, T. Jewison, A. C. Guo et al., Drugbank 4.0: shedding new light on drug metabolism, Nucleic acids research, vol.42, issue.D1, pp.1091-1097, 2013.

U. Consortium, Uniprot: a hub for protein information, Nucleic acids research, vol.43, issue.D1, pp.204-212, 2014.

K. Brian, . Shoichet, D. Irwin, D. L. Kuntz, and . Bodian, Molecular docking using shape descriptors, Journal of Computational Chemistry, vol.13, issue.3, pp.380-397, 1992.

G. Jones, P. Willett, C. Robert, . Glen, R. Andrew et al., Development and validation of a genetic algorithm for flexible docking, Journal of molecular biology, vol.267, issue.3, pp.727-748, 1997.

M. Garrett, D. S. Morris, R. S. Goodsell, R. Halliday, W. E. Huey et al., Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function, Journal of computational chemistry, vol.19, issue.14, pp.1639-1662, 1998.

. Piotr-s-gromski, B. Alon, . Henson, M. Jaros?aw, L. Granda et al., How to explore chemical space using algorithms and automation, Nature Reviews Chemistry, p.1, 2019.

C. Azencott, Statistical Machine Learning and Data Mining for Chemoinformatics and Drug Discovery, 2010.

J. , P. Vert, and L. Jacob, Machine learning for in silico virtual screening and chemical genomics: new strategies, Combinatorial chemistry & high throughput screening, vol.11, issue.8, pp.677-685, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00433569

S. Justin, B. Smith, N. Nebgen, O. Lubbers, A. E. Isayev et al., Less is more: Sampling chemical space with active learning, The Journal of chemical physics, vol.148, issue.24, p.241733, 2018.

S. Swamidass, J. A. Bittker, N. E. Bodycombe, S. P. Ryder, and P. Clemons, An economic framework to prioritize confirmatory tests after a high-throughput screen, Journal of biomolecular screening, vol.15, issue.6, pp.680-686, 2010.

A. Rachel, F. Powers, B. K. Morandi, and . Shoichet, Structure-based discovery of a novel, noncovalent inhibitor of ampc ?-lactamase, Structure, vol.10, issue.7, pp.1013-1023, 2002.

S. L. Thompson-n-doman, . Mcgovern, J. Bryan, . Witherbee, P. Thomas et al., Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1b, Journal of medicinal chemistry, vol.45, issue.11, pp.2213-2221, 2002.

A. Christopher, F. Lipinski, . Lombardo, W. Beryl, P. Dominy et al., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, vol.23, pp.3-25, 1997.

L. Andrew and . Hopkins, Network pharmacology: the next paradigm in drug discovery, Nature chemical biology, vol.4, issue.11, p.682, 2008.

H. Zhou, M. Gao, and J. Skolnick, Comprehensive prediction of drug-protein interactions and side effects for the human proteome, Scientific reports, vol.5, p.11090, 2015.

M. Campillos, M. Kuhn, A. Gavin, L. J. Jensen, and P. Bork, Drug target identification using side-effect similarity, Science, vol.321, issue.5886, pp.263-266, 2008.

T. Ted, K. B. Ashburn, and . Thor, Drug repositioning: identifying and developing new uses for existing drugs, Nature reviews Drug discovery, vol.3, issue.8, pp.673-683, 2004.

D. Ma, D. Shiu-hin-chan, and C. Leung, Drug repositioning by structure-based virtual screening, Chemical Society Reviews, vol.42, issue.5, pp.2130-2141, 2013.

K. Shameer, K. Johnson, S. Benjamin, R. Glicksberg, B. Hodos et al., Prioritizing small molecule as candidates for drug repositioning using machine learning. bioRxiv, p.331975, 2018.

B. Sanchez, -. , and A. Aspuru-guzik, Inverse molecular design using machine learning: Generative models for matter engineering, Science, vol.361, issue.6400, pp.360-365, 2018.

J. Sieg, F. Flachsenberg, and M. Rarey, In need of bias control: Evaluating chemical data for machine learning in structure-based virtual screening, Journal of chemical information and modeling, 2019.

E. Ann, A. N. Cleves, and . Jain, Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery, Journal of computer-aided molecular design, vol.22, issue.3-4, pp.147-159, 2008.

B. Schölkopf, C. John, J. Platt, A. J. Shawe-taylor, R. Smola et al., Estimating the support of a high-dimensional distribution, Neural computation, vol.13, issue.7, pp.1443-1471, 2001.

M. Larry, M. Manevitz, and . Yousef, One-class svms for document classification, the Journal of machine Learning research, vol.2, pp.139-154, 2002.

Y. Xiao, B. Liu, J. Yin, L. Cao, C. Zhang et al., Similarity-based approach for positive and unlabeled learning, IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol.22, p.1577, 2011.

N. Nagamine and Y. Sakakibara, Statistical prediction of protein-chemical interactions based on chemical structure and mass spectrometry data, Bioinformatics, vol.23, issue.15, pp.2004-2012, 2007.

R. Akbani, S. Kwek, and N. Japkowicz, Applying support vector machines to imbalanced datasets, Machine learning: ECML 2004, pp.39-50, 2004.

K. Veropoulos, C. Campbell, and N. Cristianini, Controlling the sensitivity of support vector machines, Proceedings of the international joint conference on AI, pp.55-60, 1999.

N. Ajay, A. E. Jain, and . Cleves, Does your model weigh the same as a duck, Journal of computer-aided molecular design, vol.26, issue.1, pp.57-67, 2012.

W. Tip, C. Loo, D. Bartlett, and . Clarke, Simultaneous binding of two different drugs in the binding pocket of the human multidrug resistance p-glycoprotein, Journal of Biological Chemistry, vol.278, issue.41, pp.39706-39710, 2003.

Y. Yamanishi, E. Pauwels, H. Saigo, and V. Stoven, Identification of chemogenomic features from drug-target interaction networks by sparse canonical correspondence analysis, Machine Learning in Systems Biology, p.87, 2011.

Y. Wang and J. Zeng, Predicting drug-target interactions using restricted boltzmann machines, Bioinformatics, vol.29, issue.13, pp.126-134, 2013.

S. Günther, M. Kuhn, M. Dunkel, M. Campillos, C. Senger et al., Supertarget and matador: resources for exploring drug-target relationships, Nucleic acids research, vol.36, issue.1, pp.919-922, 2008.

R. Michael, . Browning, T. Bradley, S. Calhoun, and . Swamidass, Managing missing measurements in small-molecule screens, Journal of computer-aided molecular design, vol.27, issue.5, pp.469-478, 2013.

M. Asogawa, T. Osoda, Y. Fujiwara, and Y. Yamashita, Efficient drug screening using active learning, NEC J. Adv. Technol, vol.2, issue.2, pp.145-148, 2005.

J. M-?hsan-ecemi?, C. Wikel, E. Bingham, and . Bonabeau, A drug candidate design environment using evolutionary computation. Evolutionary Computation, IEEE Transactions on, vol.12, issue.5, pp.591-603, 2008.

Y. Fujiwara, Y. Yamashita, T. Osoda, M. Asogawa, C. Fukushima et al., Virtual screening system for finding structurally diverse hits by active learning, Journal of chemical information and modeling, vol.48, issue.4, pp.930-940, 2008.

D. Reker and G. Schneider, Active-learning strategies in computer-assisted drug discovery, Drug discovery today, vol.20, issue.4, pp.458-465, 2015.

T. Gärtner, A survey of kernels for structured data, ACM SIGKDD Explorations Newsletter, vol.5, issue.1, pp.49-58, 2003.

C. Benoit-playe, V. Azencott, and . Stoven, Efficient multi-task chemogenomics for drug specificity prediction. bioRxiv, p.193391, 2017.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning. nature, vol.521, p.436, 2015.

F. James and . Blake, Chemoinformatics-predicting the physicochemical properties of 'drug-like' molecules, Current Opinion in Biotechnology, vol.11, issue.1, pp.104-107, 2000.

F. Svava-Ósk-jónsdóttir, S. Steen-jørgensen, and . Brunak, Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates, Bioinformatics, vol.21, issue.10, pp.2145-2160, 2005.

R. G. Gert, T. Lanckriet, N. De-bie, . Cristianini, W. S. Michael-i-jordan et al., A statistical framework for genomic data fusion, Bioinformatics, vol.20, issue.16, pp.2626-2635, 2004.

D. Paul, A. Dobson, and . Doig, Predicting enzyme class from protein structure without alignments, Journal of molecular biology, vol.345, issue.1, pp.187-199, 2005.

H. Timmerman, R. Todeschini, V. Consonni, R. Mannhold, and H. Kubinyi, Handbook of molecular descriptors, 2002.

D. Rogers and M. Hahn, Extended-connectivity fingerprints, Journal of chemical information and modeling, vol.50, issue.5, pp.742-754, 2010.

A. Sureyya-rifaioglu, H. Atas, M. J. Martin, R. Cetin-atalay, V. Atalay et al., Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases, Briefings in bioinformatics, 2018.

R. Todeschini and V. Consonni, Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references, vol.41, 2009.

A. Cereto-massagué, M. Ojeda, C. Valls, M. Mulero, S. Garcia-vallvé et al., Molecular fingerprint similarity search in virtual screening, Methods, vol.71, pp.58-63, 2015.

J. David, J. Wood, M. De-vlieg, T. Wagener, and . Ritschel, Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement, Journal of chemical information and modeling, vol.52, issue.8, pp.2031-2043, 2012.

D. Weininger, Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules, Journal of chemical information and computer sciences, vol.28, issue.1, pp.31-36, 1988.

A. Stephen-r-heller, I. Mcnaught, S. Pletnev, D. Stein, and . Tchekhovskoi, Inchi, the iupac international chemical identifier, Journal of cheminformatics, vol.7, issue.1, p.23, 2015.

J. Duan, . Sastry, L. Steven, J. F. Dixon, W. Lowrie et al., Analysis and comparison of 2d fingerprints: insights into database screening performance using eight fingerprint methods, Journal of cheminformatics, vol.3, issue.S1, p.1, 2011.

A. Bender, J. L. Jenkins, J. Scheiber, S. Chetan, K. Sukuru et al., How similar are similarity searching methods? a principal component analysis of molecular descriptor space, Journal of chemical information and modeling, vol.49, issue.1, pp.108-119, 2009.

D. Alberga, D. Trisciuzzi, M. Montaruli, F. Leonetti, G. F. Mangiatordi et al., A new approach for drug target and bioactivity prediction: The multifingerprint similarity search algorithm (mussel), Journal of chemical information and modeling, 2018.

S. Riniker, A. Gregory, and . Landrum, Open-source platform to benchmark fingerprints for ligandbased virtual screening, Journal of cheminformatics, vol.5, issue.1, p.26, 2013.

Z. Li, H. Huang-lin, . Ly-han, . Jiang, Y. Chen et al., Profeat: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence, Nucleic Acids Research, vol.34, issue.2, pp.32-37, 2006.

A. K. Serene, H. H. Ong, Y. Z. Lin, Z. Chen, Z. Li et al., Efficacy of different protein descriptors in predicting protein functional families, Bmc Bioinformatics, vol.8, issue.1, p.300, 2007.

H. Lodhi, C. Saunders, J. Shawe-taylor, N. Cristianini, and C. Watkins, Text classification using string kernels, The Journal of Machine Learning Research, vol.2, pp.419-444, 2002.

E. Christina-s-leslie, W. S. Eskin, and . Noble, The spectrum kernel: A string kernel for svm protein classification, Pacific symposium on biocomputing, vol.7, pp.566-575, 2002.

E. Eskin, J. Weston, S. William, C. S. Noble, and . Leslie, Mismatch string kernels for svm protein classification, Advances in neural information processing systems, pp.1417-1424, 2002.

R. Kuang, E. Ie, K. Wang, K. Wang, M. Siddiqi et al., Profilebased string kernels for remote homology detection and motif extraction, Journal of bioinformatics and computational biology, vol.3, issue.03, pp.527-550, 2005.

M. Wang, J. Yang, G. Liu, Z. Xu, and K. Chou, Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition, Protein Engineering Design and Selection, vol.17, issue.6, pp.509-516, 2004.

. Shao-wu, Q. Zhang, H. Pan, Y. Zhang, H. Zhang et al., Classification of protein quaternary structure with support vector machine, Bioinformatics, vol.19, issue.18, pp.2390-2396, 2003.

F. Temple, . Smith, and . Michael-s-waterman, Identification of common molecular subsequences, Journal of molecular biology, vol.147, issue.1, pp.195-197, 1981.

. Stephen-f-altschul, L. Thomas, A. A. Madden, J. Schäffer, Z. Zhang et al., Gapped blast and psi-blast: a new generation of protein database search programs, Nucleic acids research, vol.25, issue.17, pp.3389-3402, 1997.

. William-r-pearson, ] rapid and sensitive sequence comparison with fastp and fasta, Methods in enzymology, vol.183, issue.5, pp.63-98, 1990.

L. Liao and W. S. Noble, Combining pairwise sequence similarity and support vector machines for remote protein homology detection, Proceedings of the sixth annual international conference on Computational biology, pp.225-232, 2002.

H. Saigo, J. Vert, N. Ueda, and T. Akutsu, Protein homology detection using string alignment kernels, Bioinformatics, vol.20, issue.11, pp.1682-1689, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00433587

J. Michael, . Keiser, L. Bryan, . Roth, N. Blaine et al., Relating protein pharmacology by ligand chemistry, Nature biotechnology, vol.25, issue.2, pp.197-206, 2007.

M. Karsten, . Borgwardt, . Cheng-soon, S. Ong, . Schönauer et al., Protein function prediction via graph kernels, Bioinformatics, vol.21, issue.1, pp.47-56, 2005.

B. Hoffmann, M. Zaslavskiy, J. Vert, and V. Stoven, A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3d: application to ligand prediction, BMC bioinformatics, vol.11, issue.1, p.1, 2010.
URL : https://hal.archives-ouvertes.fr/inserm-00663528

R. Kondor, N. Shervashidze, and K. M. Borgwardt, The graphlet spectrum, Proceedings of the 26th Annual International Conference on Machine Learning, pp.529-536, 2009.

R. Darren and . Flower, On the properties of bit string-based measures of chemical similarity, Journal of Chemical Information and Computer Sciences, vol.38, issue.3, pp.379-386, 1998.

M. Hattori, Y. Okuno, S. Goto, and M. Kanehisa, Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways, Journal of the American Chemical Society, vol.125, issue.39, pp.11853-11865, 2003.

S. Swamidass, J. Chen, J. Bruand, P. Phung, L. Ralaivola et al., Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity, Bioinformatics, vol.21, issue.1, pp.359-368, 2005.

H. Kashima, K. Tsuda, and A. Inokuchi, Marginalized kernels between labeled graphs, ICML, vol.3, pp.321-328, 2003.

T. Gärtner, P. Flach, and S. Wrobel, On graph kernels: Hardness results and efficient alternatives, Learning Theory and Kernel Machines, pp.129-143, 2003.

J. Ramon and T. Gärtner, Expressivity versus efficiency of graph kernels, First international workshop on mining graphs, trees and sequences, pp.65-74, 2003.

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.939-951, 2005.

T. Horváth, Cyclic pattern kernels revisited, Advances in knowledge discovery and data mining, pp.791-801, 2005.

P. Mahé and J. Vert, Graph kernels based on tree patterns for molecules, Machine learning, vol.75, issue.1, pp.3-35, 2009.

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.

L. Jacob, B. Hoffmann, V. Stoven, and J. Vert, Virtual screening of gpcrs: an in silico chemogenomics approach, BMC bioinformatics, vol.9, issue.1, p.363, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00220396

L. Ralaivola, J. Sanjay, H. Swamidass, P. Saigo, and . Baldi, Graph kernels for chemical informatics, Neural Networks, vol.18, issue.8, pp.1093-1110, 2005.

C. Azencott, A. Ksikes, 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, Journal of chemical information and modeling, vol.47, issue.3, pp.965-974, 2007.

M. Karsten, H. Borgwardt, and . Kriegel, Shortest-path kernels on graphs, Fifth IEEE International Conference on, 2005.

T. Horváth, T. Gärtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.158-167, 2004.

N. Shervashidze, P. Schweitzer, E. J. Van-leeuwen, K. Mehlhorn, and K. M. Borgwardt, Weisfeiler-lehman graph kernels, The Journal of Machine Learning Research, vol.12, pp.2539-2561, 2011.

, Giannis Nikolentzos and Michalis Vazirgiannis. Message passing graph kernels, 2018.

K. Kersting, M. Mladenov, R. Garnett, and M. Grohe, Power iterated color refinement, AAAI, pp.1904-1910, 2014.

B. Li, X. Zhu, L. Chi, and C. Zhang, Nested subtree hash kernels for large-scale graph classification over streams, IEEE 12th International Conference on, pp.399-408, 2012.

C. Morris, M. Nils, K. Kriege, P. Kersting, and . Mutzel, Faster kernels for graphs with continuous attributes via hashing, IEEE 16th International Conference on, pp.1095-1100, 2016.

V. Arvind, J. Köbler, G. Rattan, and O. Verbitsky, On the power of color refinement, International Symposium on Fundamentals of Computation Theory, pp.339-350

. Springer, , 2015.

C. Morris, K. Kersting, and P. Mutzel, Glocalized weisfeiler-lehman graph kernels: Global-local feature maps of graphs, 2017 IEEE International Conference on, pp.327-336, 2017.

Y. Hizukuri, R. Sawada, and Y. Yamanishi, Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner, BMC medical genomics, vol.8, issue.1, p.1, 2015.

S. Mizutani, E. Pauwels, V. Stoven, S. Goto, and Y. Yamanishi, Relating drug-protein interaction network with drug side effects, Bioinformatics, vol.28, issue.18, pp.522-528, 2012.

M. Takarabe, M. Kotera, Y. Nishimura, S. Goto, and Y. Yamanishi, Drug target prediction using adverse event report systems: a pharmacogenomic approach, vol.28, pp.611-618, 2012.

E. Pauwels, V. Stoven, and Y. Yamanishi, Predicting drug side-effect profiles: a chemical fragment-based approach, BMC bioinformatics, vol.12, issue.1, p.169, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00663945

J. Lamb, D. Emily, D. Crawford, J. W. Peck, I. C. Modell et al., The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. science, vol.313, pp.1929-1935, 2006.

F. Napolitano, Y. Zhao, M. Vânia, R. Moreira, J. Tagliaferri et al., Drug repositioning: a machine-learning approach through data integration, J. Cheminformatics, vol.5, p.30, 2013.

S. Zhao and S. Li, Network-based relating pharmacological and genomic spaces for drug target identification, PloS one, vol.5, issue.7, p.11764, 2010.

. Twan-van-laarhoven, B. Sander, E. Nabuurs, and . Marchiori, Gaussian interaction profile kernels for predicting drug-target interaction, Bioinformatics, vol.27, issue.21, pp.3036-3043, 2011.

E. Twan-van-laarhoven and . Marchiori, Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile, PloS one, vol.8, issue.6, p.66952, 2013.

T. Liu, Y. Lin, X. Wen, M. Robert-n-jorissen, and . Gilson, Bindingdb: a webaccessible database of experimentally determined protein-ligand binding affinities. Nucleic acids research, vol.35, pp.198-201, 2006.

S. David, C. Wishart, A. C. Knox, S. Guo, M. Shrivastava et al., Drugbank: a comprehensive resource for in silico drug discovery and exploration, Nucleic acids research, vol.34, issue.1, pp.668-672, 2006.

M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M. Tanabe, Kegg for integration and interpretation of large-scale molecular data sets, Nucleic acids research, p.988, 2011.

G. Joshi-tope, M. Gillespie, I. Vastrik, D. Peter, E. Eustachio et al., Reactome: a knowledgebase of biological pathways, Nucleic acids research, vol.33, issue.1, pp.428-432, 2005.

M. Kuhn, D. Szklarczyk, A. Franceschini, C. V. Mering, L. J. Jensen et al., Stitch 3: zooming in on protein-chemical interactions, Nucleic acids research, vol.40, issue.D1, pp.876-880, 2011.

K. Degtyarenko, P. D. Matos, M. Ennis, J. Hastings, M. Zbinden et al., Chebi: a database and ontology for chemical entities of biological interest, Nucleic acids research, vol.36, issue.suppl_1, pp.344-350, 2007.

J. John, B. K. Irwin, and . Shoichet, Zinc-a free database of commercially available compounds for virtual screening, Journal of chemical information and modeling, vol.45, issue.1, pp.177-182, 2005.

D. Joel-l-sussman, J. Lin, N. O. Jiang, J. Manning, O. Prilusky et al., Protein data bank (pdb): database of three-dimensional structural information of biological macromolecules, Acta Crystallographica Section D, vol.54, issue.6-1, pp.1078-1084, 1998.

D. Jérémy, B. Guillaume, R. Didier, and K. Esther, sc-pdb: a 3d-database of ligandable binding sites-10 years on, Nucleic acids research, p.928, 2014.

A. Bateman, E. Birney, R. Durbin, R. Sean, . Eddy et al., Pfam 3.1: 1313 multiple alignments and profile hmms match the majority of proteins, Nucleic acids research, vol.27, issue.1, pp.260-262, 1999.

S. E. Alexey-g-murzin, T. Brenner, C. Hubbard, and . Chothia, Scop: a structural classification of proteins database for the investigation of sequences and structures, Journal of molecular biology, vol.247, issue.4, pp.536-540, 1995.

S. Hunter, R. Apweiler, T. K. Attwood, A. Bairoch, A. Bateman et al., Interpro: the integrative protein signature database, Nucleic acids research, vol.37, issue.suppl_1, pp.211-215, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01214141

M. G. Frances, . Pearl, J. E. Bennett, . Bray, P. Andrew et al., The cath database: an extended protein family resource for structural and functional genomics, Nucleic acids research, vol.31, issue.1, pp.452-455, 2003.

Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, Prediction of drug-target interaction networks from the integration of chemical and genomic spaces, Bioinformatics, vol.24, issue.13, pp.232-240, 2008.

Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework, Bioinformatics, vol.26, issue.12, pp.246-254, 2010.

K. Bleakley and Y. Yamanishi, Supervised prediction of drug-target interactions using bipartite local models, Bioinformatics, vol.25, issue.18, pp.2397-2403, 2009.

J. Mei, C. Kwoh, P. Yang, X. Li, and J. Zheng, Drug-target interaction prediction by learning from local information and neighbors, Bioinformatics, vol.29, issue.2, pp.238-245, 2013.

M. Gönen, Predicting drug-target interactions from chemical and genomic kernels using bayesian matrix factorization, Bioinformatics, vol.28, issue.18, pp.2304-2310, 2012.

M. Gonen and S. Kaski, Kernelized bayesian matrix factorization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.36, issue.10, pp.2047-2060, 2014.

L. Jacob and J. Vert, Protein-ligand interaction prediction: an improved chemogenomics approach, Bioinformatics, vol.24, issue.19, pp.2149-2156, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00433572

Y. Liu, M. Wu, C. Miao, P. Zhao, and X. Li, Neighborhood regularized logistic matrix factorization for drug-target interaction prediction, PLoS computational biology, vol.12, issue.2, p.1004760, 2016.

M. Michael, M. Mysinger, . Carchia, J. John, B. K. Irwin et al., Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking, Journal of medicinal chemistry, vol.55, issue.14, pp.6582-6594, 2012.

Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse et al., Moleculenet: a benchmark for molecular machine learning, Chemical science, vol.9, issue.2, pp.513-530, 2018.

G. Landrum, , vol.1, pp.1-79, 2013.

J. Perret and P. Mahé, Chemcpp user guide, Bioinformatics Center and Center for Computational Biology, 2006.

J. Vert, The optimal assignment kernel is not positive definite, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00218278

Z. Chen, P. Zhao, F. Li, A. Leier, T. T. Marquez-lago et al., ifeature: a python package and web server for features extraction and selection from protein and peptide sequences, Bioinformatics, vol.34, issue.14, pp.2499-2502, 2018.

N. Xiao, D. Cao, M. Zhu, and Q. Xu, protr/protrweb: R package and web server for generating various numerical representation schemes of protein sequences, Bioinformatics, vol.31, issue.11, pp.1857-1859, 2015.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis et al., Tensorflow: a system for large-scale machine learning, OSDI, vol.16, pp.265-283, 2016.

F. Chollet, , 2015.

. Gerard-jp-van-westen, K. Jörg, . Wegner, P. Adriaan, . Ijzerman et al., Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets, MedChemComm, vol.2, issue.1, pp.16-30, 2011.

M. Hao, H. Stephen, Y. Bryant, and . Wang, A new chemoinformatics approach with improved strategies for effective predictions of potential drugs, Journal of cheminformatics, vol.10, issue.1, p.50, 2018.

Y. Martinez-lopez, Y. Caballero, J. Stephen, Y. Barigye, R. Marrero-ponce et al., State of the art review and report of new tool for drug discovery, vol.17, pp.2957-2976, 2017.

Y. Yamanishi, Inferring chemogenomic features from drug-target interaction networks, Molecular Informatics, vol.32, pp.991-999, 2013.

Q. Yuan, J. Gao, D. Wu, S. Zhang, H. Mamitsuka et al.,

, Druge-rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank, Bioinformatics, vol.32, issue.12, pp.18-27, 2016.

Z. Xia, L. Wu, X. Zhou, . Stephen, and . Wong, Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces, BMC systems biology, vol.4, issue.2, p.6, 2010.

X. Zheng, H. Ding, H. Mamitsuka, and S. Zhu, Collaborative matrix factorization with multiple similarities for predicting drug-target interactions, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1025-1033, 2013.

J. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in large margin classifiers, vol.10, issue.3, pp.61-74, 1999.

C. Christopher and . Johnson, Logistic matrix factorization for implicit feedback data, Advances in Neural Information Processing Systems, vol.27, 2014.

. Murat-can, C. Cobanoglu, F. Liu, . Hu, I. Oltvai et al., Predicting drug-target interactions using probabilistic matrix factorization, Journal of chemical information and modeling, vol.53, issue.12, pp.3399-3409, 2013.

H. Yu, J. Chen, X. Xu, Y. Li, H. Zhao et al., A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data, PloS one, vol.7, issue.5, p.37608, 2012.

D. Cao, L. Zhang, G. Tan, and Z. Xiang,

A. Chen, Computational prediction of drug-target interactions using chemical, biological, and network features, Molecular Informatics, vol.33, issue.10, pp.669-681, 2014.

L. Breiman, Random forests. Machine learning, vol.45, pp.5-32, 2001.

S. Swamidass, C. Azencott, T. Lin, and H. Gramajo,

P. Baldi, Influence relevance voting: an accurate and interpretable virtual high throughput screening method, Journal of chemical information and modeling, vol.49, issue.4, pp.756-766, 2009.

E. Geoffrey, . Hinton, R. Ruslan, and . Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, vol.313, issue.5786, pp.504-507, 2006.

T. He, M. Heidemeyer, F. Ban, A. Cherkasov, and M. Ester, Simboost: a readacross approach for predicting drug-target binding affinities using gradient boosting machines, Journal of cheminformatics, vol.9, issue.1, p.24, 2017.

R. Zhang, An ensemble learning approach for improving drug-target interactions prediction, Proceedings of the 4th International Conference on Computer Engineering and Networks, pp.433-442, 2015.

M. Hao, Y. Wang, and S. Bryant, Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique, Analytica chimica acta, vol.909, pp.41-50, 2016.

H. Rawan-s-olayan, V. B. Ashoor, and . Bajic, Ddr: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches, Bioinformatics, vol.34, issue.7, pp.1164-1173, 2017.

C. A. André, R. Nascimento, I. Bc-prudêncio, and . Costa, A multiple kernel learning algorithm for drug-target interaction prediction, BMC bioinformatics, vol.17, issue.1, p.1, 2016.

A. Cichonska, T. Pahikkala, S. Szedmak, H. Julkunen, A. Airola et al., Learning with multiple pairwise kernels for drug bioactivity prediction, Bioinformatics, vol.34, issue.13, pp.509-518, 2018.

T. Pahikkala, A. Airola, S. Pietilä, S. Shakyawar, A. Szwajda et al., Toward more realistic drug-target interaction predictions, Briefings in bioinformatics, p.10, 2014.

C. Cortes and V. Vapnik, Support-vector networks, Machine learning, vol.20, issue.3, pp.273-297, 1995.

D. Erhan, P. Heureux, Y. Shi, Y. Yue, and . Bengio, Collaborative filtering on a family of biological targets, Journal of chemical information and modeling, vol.46, issue.2, pp.626-635, 2006.

J. Faulon, M. Misra, S. Martin, K. Sale, and R. Sapra, Genome scale enzymemetabolite and drug-target interaction predictions using the signature molecular descriptor, vol.24, pp.225-233, 2008.

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.

Y. Okuno, A. Tamon, H. Yabuuchi, S. Niijima, Y. Minowa et al., Glida: Gpcr-ligand database for chemical genomics drug discovery-database and tools update, Nucleic acids research, vol.36, issue.suppl_1, pp.907-912, 2007.

G. Manning, B. David, R. Whyte, T. Martinez, S. Hunter et al., The protein kinase complement of the human genome, Science, vol.298, issue.5600, pp.1912-1934, 2002.

M. Kanehisa, M. Araki, S. Goto, M. Hattori, M. Hirakawa et al., Kegg for linking genomes to life and the environment, Nucleic acids research, vol.36, issue.suppl_1, pp.480-484, 2007.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, Springer series in statistics, vol.1, 2001.

A. James, B. J. Hanley, and . Mcneil, The meaning and use of the area under a receiver operating characteristic (roc) curve, Radiology, vol.143, issue.1, pp.29-36, 1982.

V. Raghavan, P. Bollmann, and G. Jung, A critical investigation of recall and precision as measures of retrieval system performance, ACM Transactions on Information Systems (TOIS), vol.7, issue.3, pp.205-229, 1989.

C. Kramer and P. Gedeck, Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets, Journal of chemical information and modeling, vol.50, issue.11, pp.1961-1969, 2010.

C. Stephen and . Johnson, Hierarchical clustering schemes, Psychometrika, vol.32, issue.3, pp.241-254, 1967.

J. Manfred-k-warmuth, G. Liao, M. Rätsch, S. Mathieson, C. Putta et al., Active learning with support vector machines in the drug discovery process, Journal of Chemical Information and Computer Sciences, vol.43, issue.2, pp.667-673, 2003.

M. Fung, A. Thornton, K. Mybeck, J. Hsiao-hui-wu, K. Hornbuckle et al., Evaluation of the characteristics of safety withdrawal of prescription drugs from worldwide pharmaceutical markets-1960 to 1999, Drug Information Journal, vol.35, issue.1, pp.293-317, 2001.

P. Zaina, E. Qureshi, R. Seoane-vazquez, K. B. Rodriguez-monguio, S. L. Stevenson et al., Market withdrawal of new molecular entities approved in the united states from 1980 to, Pharmacoepidemiology and drug safety, vol.20, issue.7, pp.772-777, 2009.

A. Fabr?cio and . Moreira, Best practice & research clinical endocrinology & metabolism, Best Practice & Research Clinical Endocrinology & Metabolism, vol.23, pp.133-144, 2009.

. Jm-cotton, . Kearney, and . Shah, Nitric oxide and myocardial function in heart failure: friend or foe?, Heart, vol.88, issue.6, pp.564-566, 2002.

A. Kazakov, R. Hall, P. Jagoda, K. Bachelier, P. Müller-best et al., Inhibition of endothelial nitric oxide synthase induces and enhances myocardial fibrosis, Cardiovascular research, vol.100, issue.2, pp.211-221, 2013.

X. Zhang, D. K. Lieu, and N. Chiamvimonvat, Small-conductance ca 2+-activated k+ channels and cardiac arrhythmias, Heart Rhythm, vol.12, issue.8, pp.1845-1851, 2015.

T. Aoyagi and T. Matsui, Phosphoinositide-3 kinase signaling in cardiac hypertrophy and heart failure, Current pharmaceutical design, vol.17, issue.18, pp.1818-1824, 2011.

B. Christiansen, A. Meinild, A. A. Jensen, and H. Braüner-osborne, Cloning and characterization of a functional human ?-aminobutyric acid (gaba) transporter, human gat-2, Journal of Biological Chemistry, vol.282, issue.27, pp.19331-19341, 2007.

L. Deng, G. Hinton, and B. Kingsbury, New types of deep neural network learning for speech recognition and related applications: An overview, Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp.8599-8603, 2013.

C. Angermueller, T. Pärnamaa, L. Parts, and O. Stegle, Deep learning for computational biology, Molecular Systems Biology, vol.12, issue.7, p.878, 2016.

B. Seonwoo-min, S. Lee, and . Yoon, Deep learning in bioinformatics, Briefings in bioinformatics, vol.18, issue.5, pp.851-869, 2017.

M. Philip, B. J. Farrell, . Rosenstein, B. Terry, . White et al., Guidelines for diagnosis of cystic fibrosis in newborns through older adults: Cystic fibrosis foundation consensus report, The Journal of pediatrics, vol.153, issue.2, pp.4-14, 2008.

M. Johanna, . Rommens, C. Michael, . Iannuzzi, . Bat-sheva et al., Identification of the cystic fibrosis gene: chromosome walking and jumping, Science, vol.245, issue.4922, pp.1059-1065, 1989.

F. Christopher and . Higgins, Abc transporters: from microorganisms to man, Annual review of cell biology, vol.8, issue.1, pp.67-113, 1992.

E. O. Ann, M. Trezíse, and . Buchwald, In vivo cell-specific expression of the cystic fibrosis transmembrane conductance regulator, Nature, vol.353, issue.6343, p.434, 1991.

P. Linsdell and . John-w-hanrahan, Glutathione permeability of cftr, American Journal of PhysiologyCell Physiology, vol.275, issue.1, pp.323-326, 1998.

P. Moskwa, D. Lorentzen, J. Katherine, J. Excoffon, P. B. Zabner et al., A novel host defense system of airways is defective in cystic fibrosis, American journal of respiratory and critical care medicine, vol.175, issue.2, pp.174-183, 2007.

A. David, . Stoltz, K. David, M. Meyerholz, and . Welsh, Origins of cystic fibrosis lung disease, New England Journal of Medicine, vol.372, issue.4, pp.351-362, 2015.

F. Galli, A. Battistoni, R. Gambari, A. Pompella, A. Bragonzi et al., Oxidative stress and antioxidant therapy in cystic fibrosis, Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, vol.1822, issue.5, pp.690-713, 2012.

. Js-elborn, , vol.29, pp.576-582, 2016.

. Y-alahdab-ozen and . Deniz-g-duman, Pancreatic involvement in cystic fibrosis, Minerva medica, vol.107, issue.6, pp.427-436, 2016.

N. Kamal, P. Surana, and C. Koh, Liver disease in patients with cystic fibrosis, Current opinion in gastroenterology, vol.34, issue.3, pp.146-151, 2018.

L. Tracey, . Bonfield, M. Michael-w-konstan, and . Berger, Altered respiratory epithelial cell cytokine production in cystic fibrosis, Journal of Allergy and Clinical Immunology, vol.104, issue.1, pp.72-78, 1999.

X. Xia, J. Wang, Y. Liu, and M. Yue, Lower cystic fibrosis transmembrane conductance regulator (cftr) promotes the proliferation and migration of endometrial carcinoma, Medical science monitor: international medical journal of experimental and clinical research, vol.23, p.966, 2017.

X. Zhong, H. Chen, X. Yang, Q. Wang, W. Chen et al., Cftr activation suppresses glioblastoma cell proliferation, migration and invasion, Biochemical and biophysical research communications, vol.508, issue.4, pp.1279-1285, 2019.

M. Bodas and N. Vij, Adapting proteostasis and autophagy for controlling the pathogenesis of cystic fibrosis lung disease, Frontiers in pharmacology, vol.10, 2019.

V. Ogilvie, M. Passmore, L. Hyndman, L. Jones, B. Stevenson et al., Differential global gene expression in cystic fibrosis nasal and bronchial epithelium, Genomics, vol.98, issue.5, pp.327-336, 2011.

T. Mackenzie, A. H. Gifford, K. A. Sabadosa, B. Hebe, E. A. Quinton et al., Longevity of patients with cystic fibrosis in 2000 to 2010 and beyond: survival analysis of the cystic fibrosis foundation patient registry, Annals of internal medicine, vol.161, issue.4, pp.233-241, 2014.

X. Meng, J. Clews, V. Kargas, X. Wang, and R. Ford, The cystic fibrosis transmembrane conductance regulator (cftr) and its stability, Cellular and Molecular Life Sciences, vol.74, issue.1, pp.23-38, 2017.

B. Anjaparavanda-p-naren, C. Cobb, K. Li, D. Roy, . Nelson et al., A macromolecular complex of ?2 adrenergic receptor, cftr, and ezrin/radixin/moesin-binding phosphoprotein 50 is regulated by pka, Proceedings of the National Academy of Sciences, vol.100, issue.1, pp.342-346, 2003.

P. Vergani, W. Steve, A. C. Lockless, D. Nairn, and . Gadsby, Cftr channel opening by atp-driven tight dimerization of its nucleotide-binding domains, Nature, vol.433, issue.7028, p.876, 2005.

F. Marson, C. S. Bertuzzo, and J. Ribeiro, Classification of cftr mutation classes, The Lancet Respiratory Medicine, vol.4, issue.8, pp.37-38, 2016.

M. Ruffin, L. Roussel, É. Maillé, S. Rousseau, and E. Brochiero, Vx-809/vx-770 treatment reduces inflammatory response to pseudomonas aeruginosa in primary differentiated cystic fibrosis bronchial epithelial cells, American Journal of Physiology-Lung Cellular and Molecular Physiology, 2017.

S. L. Steven-m-rowe, T. Heltshe, . Gonska, H. Scott, D. Donaldson et al., Clinical mechanism of the cystic fibrosis transmembrane conductance regulator potentiator ivacaftor in g551d-mediated cystic fibrosis, American journal of respiratory and critical care medicine, vol.190, issue.2, pp.175-184, 2014.

H. Yu-ren, D. E. Grove, O. Rosa, A. Scott, P. Houck et al., Vx-809 corrects folding defects in cystic fibrosis transmembrane conductance regulator protein through action on membrane-spanning domain 1, Molecular biology of the cell, vol.24, issue.19, pp.3016-3024, 2013.

F. Van-goor, S. Hadida, D. J. Peter, B. Grootenhuis, . Burton et al., Correction of the f508del-cftr protein processing defect in vitro by the investigational drug vx-809, Proceedings of the National Academy of Sciences, vol.108, issue.46, pp.18843-18848, 2011.

D. W. Paul, C. Eckford, M. Li, C. E. Ramjeesingh, and . Bear, Cystic fibrosis transmembrane conductance regulator (cftr) potentiator vx-770 (ivacaftor) opens the defective channel gate of mutant cftr in a phosphorylation-dependent but atp-independent manner, Journal of Biological Chemistry, vol.287, issue.44, pp.36639-36649, 2012.

S. Zhang, L. Chandra, B. Shrestha, and . Kopp, Cystic fibrosis transmembrane conductance regulator (cftr) modulators have differential effects on cystic fibrosis macrophage function, Scientific reports, vol.8, issue.1, p.17066, 2018.

R. Barnaby, K. Koeppen, A. Nymon, H. Thomas, B. Hampton et al., Lumacaftor (vx-809) restores the ability of cf macrophages to phagocytose and kill pseudomonas aeruginosa, American Journal of Physiology-Lung Cellular and Molecular Physiology, 2017.

K. Elena, R. M. Schneider, . Mcquade, C. Vincenzo, F. Carbone et al., The potentially beneficial central nervous system activity profile of ivacaftor and its metabolites, ERJ open research, vol.4, issue.1, pp.127-2017, 2018.

K. Elena and . Schneider, Cytochrome p450 3a4 induction: lumacaftor versus ivacaftor potentially resulting in significantly reduced plasma concentration of ivacaftor, Drug metabolism letters, vol.12, issue.1, pp.71-74, 2018.

K. Vamshi, B. Manda, . Avula, Z. Olivia-r-dale, . Ali et al., Pxr mediated induction of cyp3a4, cyp1a2, and p-gp by mitragyna speciosa and its alkaloids, Phytotherapy research, vol.31, issue.12, pp.1935-1945, 2017.

A. Brittany, . Bruch, B. Sachinkumar, L. J. Singh, T. D. Ramsey et al., Impact of a cystic fibrosis transmembrane conductance regulator (cftr) modulator on high-dose ibuprofen therapy in pediatric cystic fibrosis patients, Pediatric pulmonology, vol.53, issue.8, pp.1035-1039, 2018.

S. Jennifer, . Guimbellot, P. Edward, S. Acosta, and . Rowe, Sensitivity of ivacaftor to drug-drug interactions with rifampin, a cytochrome p450 3a4 inducer, Pediatric pulmonology, vol.53, issue.5, pp.6-8, 2018.

G. Shmarina, A. Pukhalsky, N. Petrova, E. Zakharova, L. Avakian et al., Tnf gene polymorphisms in cystic fibrosis patients: contribution to the disease progression, Journal of translational medicine, vol.11, issue.1, p.19, 2013.

J. Rocca, S. Manin, A. Hulin, A. Aissat, W. Verbecq-morlot et al., New use for an old drug: Cox-independent anti-inflammatory effects of sulindac in models of cystic fibrosis, British journal of pharmacology, vol.173, issue.11, pp.1728-1741, 2016.

. Nicola-j-ronan, G. Gisli, M. Einarsson, D. Twomey, D. Mooney et al., Cork study in cystic fibrosis: sustained improvements in ultra-low-dose chest ct scores after cftr modulation with ivacaftor, Chest, vol.153, issue.2, pp.395-403, 2018.

D. De-stefano, R. Valeria, S. Villella, A. Esposito, A. Tosco et al., Restoration of cftr function in patients with cystic fibrosis carrying the f508del-cftr mutation, Autophagy, vol.10, issue.11, pp.2053-2074, 2014.

A. S. Molly-m-he, J. D. Smith, . Oslob, M. William, A. C. Flanagan et al., Small-molecule inhibition of tnf-?, Science, vol.310, issue.5750, pp.1022-1025, 2005.

. Elizabeth-r-peitzman, A. Nathan, . Zaidman, J. Peter, . Maniak et al., Carvedilol binding to ?2-adrenergic receptors inhibits cftr-dependent anion secretion in airway epithelial cells, American Journal of Physiology-Lung Cellular and Molecular Physiology, vol.310, issue.1, pp.50-58, 2015.

J. Kim, M. Farahmand, C. Dunn, C. E. Milla, R. I. Horii et al., Sweat rate analysis of ivacaftor potentiation of cftr in non-cf adults, Scientific reports, vol.8, issue.1, p.16233, 2018.

J. John-j-brewington, A. Backstrom, E. L. Feldman, J. D. Kramer, A. J. Moncivaiz et al., Chronic ?2ar stimulation limits cftr activation in human airway epithelia, JCI insight, vol.3, issue.4, 2018.

D. W. Paul, C. Eckford, M. Li, C. E. Ramjeesingh, and . Bear, Cftr potentiator vx-770 (ivacaftor) opens the defective channel gate of mutant cftr in a phosphorylation-dependent but atp-independent manner, Journal of Biological Chemistry, p.112, 2012.

A. Carlos-m-farinha, . Swiatecka-urban, P. David-l-brautigan, and . Jordan, Regulatory crosstalk by protein kinases on cftr trafficking and activity, Frontiers in chemistry, vol.4, issue.1, 2016.

R. Fiorotto, M. Amenduni, V. Mariotti, L. Fabris, C. Spirli et al., Src kinase inhibition reduces inflammatory and cytoskeletal changes in ?f508 human cholangiocytes and improves cystic fibrosis transmembrane conductance regulator correctors efficacy, Hepatology, vol.67, issue.3, pp.972-988, 2018.

Y. Fei, L. Sun, C. Yuan, M. Jiang, Q. Lou et al., Cftr ameliorates high glucose-induced oxidative stress and inflammation by mediating the nf-?b and mapk signaling pathways in endothelial cells, International journal of molecular medicine, vol.41, issue.6, pp.3501-3508, 2018.

L. Agata-m-trzci?ska-daneluti, C. Nguyen, C. Jiang, D. Fladd, M. Uehling et al., Rima Al-awar, and Daniela Rotin. Use of kinase inhibitors to correct ?f508-cftr function

, Celltiter-glo R luminescent cell viability assay, pp.2019-2023

G. Prado-vázquez, A. Gámez-pozo, L. Trilla-fuertes, J. M. Arevalillo, A. Zapater-moros et al.,

P. Maín, A novel approach to triple-negative breast cancer molecular classification reveals a luminal immune-positive subgroup with good prognoses, Scientific reports, vol.9, issue.1, p.1538, 2019.

D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller et al., String v10: protein-protein interaction networks, integrated over the tree of life, Nucleic acids research, vol.43, issue.D1, pp.447-452, 2014.

R. Kondor and J. Vert, Diffusion kernels. kernel methods in computational biology, pp.171-192, 2004.

S. Irina, N. Babina, and . Turner, Advances and challenges in targeting fgfr signalling in cancer, Nature Reviews Cancer, vol.17, issue.5, p.318, 2017.

Z. Karoulia, E. Gavathiotis, and P. Poulikakos, New perspectives for targeting raf kinase in human cancer, Nature Reviews Cancer, vol.17, issue.11, p.676, 2017.

V. Matthew, R. J. Heiden, and . Deberardinis, Understanding the intersections between metabolism and cancer biology, Cell, vol.168, issue.4, pp.657-669, 2017.

D. Roy, Y. Gao, S. Sheng, E. Herve, A. Carvalho et al., Interplay between cancer cell cycle and metabolism: Challenges, targets and therapeutic opportunities, Biomedicine & Pharmacotherapy, vol.89, pp.288-296, 2017.

, Unknown ref

E. Breuer, D. Fukushiro-lopes, A. Dalheim, M. Burnette, J. Zartman et al., Potassium channel activity controls breast cancer metastasis by affecting ?-catenin signaling, Cell death & disease, vol.10, issue.3, p.180, 2019.

W. Keith-a-dookeran, L. Zhang, M. Stayner, and . Argos, Associations of two-pore domain potassium channels and triple negative breast cancer subtype in the cancer genome atlas: systematic evaluation of gene expression and methylation, BMC research notes, vol.10, issue.1, p.475, 2017.

A. Panaccione, Y. Guo, W. G. Yarbrough, V. Sergey, and . Ivanov, Expression profiling of clinical specimens supports the existence of neural progenitor-like stem cells in basal breast cancers, Clinical breast cancer, vol.17, issue.4, pp.298-306, 2017.

P. Zhang, X. Yang, Q. Yin, J. Yi, W. Shen et al., Inhibition of sk4 potassium channels suppresses cell proliferation, migration and the epithelialmesenchymal transition in triple-negative breast cancer cells, PloS one, vol.11, issue.4, p.154471, 2016.

J. Barretina, G. Caponigro, N. Stransky, K. Venkatesan, S. Adam-a-margolin et al., The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity, Nature, vol.483, issue.7391, p.603, 2012.

J. Mathew, E. J. Garnett, S. J. Edelman, C. D. Heidorn, A. Greenman et al., Systematic identification of genomic markers of drug sensitivity in cancer cells, Nature, vol.483, issue.7391, p.570, 2012.

B. Haibe-kains, N. El-hachem, N. J. Birkbak, C. Andrew, . Jin et al., Inconsistency in large pharmacogenomic studies, Nature, vol.504, issue.7480, p.389, 2013.

, Cancer Cell Line Encyclopedia Consortium, Genomics of Drug Sensitivity in Cancer Consortium, et al. Pharmacogenomic agreement between two cancer cell line data sets, Nature, vol.528, issue.7580, p.84, 2015.

N. Pozdeyev, M. Yoo, R. Mackie, R. E. Schweppe, . Aik-choon-tan et al., Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies, Oncotarget, vol.7, issue.32, p.51619, 2016.

A. Kraskov, H. Stögbauer, and P. Grassberger, Estimating mutual information, Physical review E, vol.69, issue.6, p.66138, 2004.

S. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, An interiorpoint method for large-scale l1-regularized least squares, IEEE journal of selected topics in signal processing, vol.1, issue.4, pp.606-617, 2007.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of statistical software, vol.33, issue.1, p.1, 2010.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, issue.4, pp.417-473, 2010.

. Mammaprint-website, , pp.2019-2023

N. Aben, J. Daniel, M. Vis, L. Michaut, and . Wessels, Tandem: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types, Bioinformatics, vol.32, issue.17, pp.413-420, 2016.

L. Rampasek, D. Hidru, P. Smirnov, B. Haibe-kains, A. Goldenberg et al., vae: Drug response variational autoencoder, 2017.

C. Chang, L. Rampasek, and A. Goldenberg, Dropout feature ranking for deep learning models, 2017.

M. Le-morvan and J. Vert, Whinter: A working set algorithm for high-dimensional sparse second order interaction models, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01711018

S. Madhu, H. Dhar, and . Plummer, Protein expression of g-protein inwardly rectifying potassium channels (girk) in breast cancer cells, BMC physiology, vol.6, issue.1, 2006.

K. Bradley, . Stringer, G. Amiel, S. Cooper, and . Shepard, Overexpression of the g-protein inwardly rectifying potassium channel 1 (girk1) in primary breast carcinomas correlates with axillary lymph node metastasis, Cancer research, vol.61, issue.2, pp.582-588, 2001.

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.1798-1828, 2013.

Y. Bengio, Learning deep architectures for ai. Foundations and trends R in Machine Learning, vol.2, pp.1-127, 2009.

T. David and . Jones, Protein secondary structure prediction based on position-specific scoring matrices1, Journal of molecular biology, vol.292, issue.2, pp.195-202, 1999.

E. Asgari, R. K. Mohammad, and . Mofrad, Continuous distributed representation of biological sequences for deep proteomics and genomics, PloS one, vol.10, issue.11, p.141287, 2015.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, 2013.

S. Wang, S. Weng, J. Ma, and Q. Tang, Deepcnf-d: predicting protein order/disorder regions by weighted deep convolutional neural fields, International journal of molecular sciences, vol.16, issue.8, pp.17315-17330, 2015.

J. Lyons, A. Dehzangi, R. Heffernan, A. Sharma, K. Paliwal et al., Predicting backbone c? angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network, Journal of computational chemistry, vol.35, issue.28, pp.2040-2046, 2014.

K. Søren, A. Riis, and . Krogh, Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments, Journal of Computational Biology, vol.3, issue.1, pp.163-183, 1996.

K. Søren, O. Sønderby, and . Winther, Protein secondary structure prediction with long short term memory networks, 2014.

M. Agathocleous, G. Christodoulou, V. Promponas, and C. Christodoulou, Protein secondary structure prediction with bidirectional recurrent neural nets: Can weight updating for each residue enhance performance, IFIP International Conference on Artificial Intelligence Applications and Innovations, pp.128-137, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01060660

V. I. Jurtz, A. R. Johansen, M. Nielsen, J. Juan-almagro-armenteros, H. Nielsen et al., An introduction to deep learning on biological sequence data: examples and solutions, Bioinformatics, vol.33, issue.22, pp.3685-3690, 2017.

Y. Qi, M. Oja, J. Weston, and W. S. Noble, A unified multitask architecture for predicting local protein properties, PloS one, vol.7, issue.3, p.32235, 2012.

F. Wan and J. Zeng, Deep learning with feature embedding for compound-protein interaction prediction. bioRxiv, p.86033, 2016.

S. Jaeger, S. Fulle, and S. Turk, Mol2vec: unsupervised machine learning approach with chemical intuition, Journal of chemical information and modeling, vol.58, issue.1, pp.27-35, 2018.

B. Garrett, N. O. Goh, C. Hodas, A. Siegel, and . Vishnu, Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties, 2017.

X. Zhang, S. Wang, F. Zhu, Z. Xu, Y. Wang et al., Seq3seq fingerprint: Towards end-to-end semi-supervised deep drug discovery, Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp.404-413, 2018.

B. Talia, S. Kimber, . Engelke, V. Igor, E. Tetko et al., Synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction, 2018.

A. Dalke, Deepsmiles: An adaptation of smiles for use in, 2018.

D. David-k-duvenaud, J. Maclaurin, R. Iparraguirre, T. Bombarell, A. Hirzel et al., Convolutional networks on graphs for learning molecular fingerprints, Advances in neural information processing systems, pp.2224-2232, 2015.

S. Kwon and S. Yoon, Deepcci: End-to-end deep learning for chemical-chemical interaction prediction, Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp.203-212, 2017.

Z. Xu, S. Wang, F. Zhu, and J. Huang, Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery, Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp.285-294, 2017.

F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda et al., Geometric deep learning on graphs and manifolds using mixture model cnns, Proc

W. Hamilton, Z. Ying, and J. Leskovec, Inductive representation learning on large graphs, Advances in Neural Information Processing Systems, pp.1024-1034, 2017.

R. William-l-hamilton, J. Ying, and . Leskovec, Representation learning on graphs: Methods and applications, 2017.

A. Dinh-v-tran and . Sperduti, On filter size in graph convolutional networks, 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp.1534-1541, 2018.

J. Chen, T. Ma, and C. Xiao, Fastgcn: fast learning with graph convolutional networks via importance sampling, 2018.

P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio et al., Graph attention networks, 2017.

H. Dai, B. Dai, and L. Song, Discriminative embeddings of latent variable models for structured data, International Conference on Machine Learning, pp.2702-2711, 2016.

A. Lusci, G. Pollastri, and P. Baldi, Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules, Journal of chemical information and modeling, vol.53, issue.7, pp.1563-1575, 2013.

W. Connor, R. Coley, . Barzilay, H. William, T. S. Green et al., Convolutional embedding of attributed molecular graphs for physical property prediction, Journal of chemical information and modeling, vol.57, issue.8, pp.1757-1772, 2017.

T. Kipf, E. Fetaya, K. Wang, M. Welling, and R. Zemel, Neural relational inference for interacting systems, 2018.

S. Kearnes, K. Mccloskey, M. Berndl, V. Pande, and P. Riley, Molecular graph convolutions: moving beyond fingerprints, Journal of computer-aided molecular design, vol.30, issue.8, pp.595-608, 2016.

S. Gadiya, D. Anand, and A. Sethi, Some new layer architectures for graph cnn, 2018.

A. Santoro, D. Raposo, G. David, M. Barrett, R. Malinowski et al., A simple neural network module for relational reasoning, Advances in neural information processing systems, pp.4967-4976, 2017.

J. Gilmer, S. Samuel, . Schoenholz, F. Patrick, O. Riley et al., Neural message passing for quantum chemistry, 2017.

M. Schlichtkrull, N. Thomas, P. Kipf, R. Bloem, . Van-den et al., Modeling relational data with graph convolutional networks, European Semantic Web Conference, pp.593-607, 2018.

C. Shang, Q. Liu, K. Chen, J. Sun, J. Lu et al., Edge attention-based multi-relational graph convolutional networks, 2018.

O. Vinyals, S. Bengio, and M. Kudlur, Order matters: Sequence to sequence for sets, 2015.

H. Altae-tran, B. Ramsundar, S. Aneesh, V. Pappu, and . Pande, Low data drug discovery with one-shot learning, ACS central science, vol.3, issue.4, pp.283-293, 2017.

S. Kearnes, B. Goldman, and V. Pande, Modeling industrial admet data with multitask networks, 2016.

J. Lee, A. Ryan, S. Rossi, . Kim, K. Nesreen et al., Attention models in graphs: A survey, 2018.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention is all you need, Advances in Neural Information Processing Systems, pp.5998-6008, 2017.

U. Shankar-shanthamallu, J. Jayaraman, A. Thiagarajan, and . Spanias, Improving robustness of attention models on graphs, 2018.

Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, Gated graph sequence neural networks, 2015.

J. Lee, R. Rossi, and X. Kong, Graph classification using structural attention, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.1666-1674, 2018.

B. Perozzi, R. Al-rfou, and S. Skiena, Deepwalk: Online learning of social representations, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.701-710, 2014.

A. Grover and J. Leskovec, node2vec: Scalable feature learning for networks, Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.855-864, 2016.

F. Scarselli, M. Gori, A. Chung-tsoi, M. Hagenbuchner, and G. Monfardini, The graph neural network model, IEEE Transactions on Neural Networks, vol.20, issue.1, pp.61-80, 2009.

M. Gori, G. Monfardini, and F. Scarselli, A new model for learning in graph domains, Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on, vol.2, pp.729-734, 2005.

T. Pham, T. Tran, H. Dam, and S. Venkatesh, Graph classification via deep learning with virtual nodes, 2017.

K. Ishiguro, M. Shin-ichi-maeda, and . Koyama, Graph warp module: an auxiliary module for boosting the power of graph neural networks, 2019.

R. Ying, J. You, C. Morris, X. Ren, L. William et al., Hierarchical graph representation learning withdifferentiable pooling, 2018.

A. Porrello, D. Abati, S. Calderara, and R. Cucchiara, Classifying signals on irregular domains via convolutional cluster pooling, 2019.

N. Thomas, M. Kipf, and . Welling, Semi-supervised classification with graph convolutional networks, 2016.

K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi et al., Representation learning on graphs with jumping knowledge networks, 2018.

G. E. Hinton, S. Osindero, and Y. Teh, A fast learning algorithm for deep belief nets, Neural computation, vol.18, issue.7, pp.1527-1554, 2006.

J. Pérez-sianes, H. Pérez-sánchez, and F. Díaz, Virtual screening: a challenge for deep learning, 10th International Conference on Practical Applications of Computational Biology & Bioinformatics, pp.13-22, 2016.

T. Unterthiner, A. Mayr, G. Klambauer, M. Steijaert, K. Jörg et al., Deep learning as an opportunity in virtual screening, Proceedings of the deep learning workshop at NIPS, vol.27, pp.1-9, 2014.

N. T. Eelke-b-lenselink, B. Dijke, G. Bongers, . Papadatos, W. Herman-wt-van-vlijmen et al., Beyond the hype: deep neural networks outperform established methods using a chembl bioactivity benchmark set, Journal of cheminformatics, vol.9, issue.1, p.45, 2017.

M. Hamanaka, K. Taneishi, H. Iwata, J. Ye, J. Pei et al., Cgbvs-dnn: Prediction of compound-protein interactions based on deep learning, Molecular Informatics, 2016.

C. Wang, J. Liu, F. Luo, Y. Tan, Z. Deng et al., Pairwise input neural network for target-ligand interaction prediction, Bioinformatics and Biomedicine (BIBM), 2014.

, IEEE International Conference on, pp.67-70, 2014.

A. Gonczarek, M. Jakub, S. Tomczak, J. Zar?ba, P. Kaczmar et al.,

J. Micha? and . Walczak, Learning deep architectures for interaction prediction in structure-based virtual screening, 2016.

E. George, N. Dahl, R. Jaitly, and . Salakhutdinov, Multi-task neural networks for qsar predictions, 2014.

J. Ma, P. Robert, A. Sheridan, G. E. Liaw, V. Dahl et al., Deep neural nets as a method for quantitative structure-activity relationships, Journal of chemical information and modeling, vol.55, issue.2, pp.263-274, 2015.

B. Ramsundar, S. Kearnes, P. Riley, D. Webster, D. Konerding et al., Massively multitask networks for drug discovery, 2015.

Y. Xu, J. Ma, A. Liaw, P. Robert, V. Sheridan et al., Demystifying multitask deep neural networks for quantitative structure-activity relationships, Journal of chemical information and modeling, vol.57, issue.10, pp.2490-2504, 2017.

A. Korotcov, V. Tkachenko, P. Daniel, S. Russo, and . Ekins, Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets, Molecular pharmaceutics, vol.14, issue.12, pp.4462-4475, 2017.

M. Vogt, S. Jasial, and J. Bajorath, Extracting compound profiling matrices from screening data, ACS Omega, vol.3, issue.4, pp.4706-4712, 2018.

E. Gawehn, J. A. Hiss, and G. Schneider, Deep learning in drug discovery, Molecular Informatics, vol.35, issue.1, pp.3-14, 2016.

A. Mayr, G. Klambauer, T. Unterthiner, and S. Hochreiter, Deeptox: toxicity prediction using deep learning, Frontiers in Environmental Science, vol.3, p.80, 2016.

A. Karim, A. Mishra, A. Ma-hakim-newton, and . Sattar, Efficient toxicity prediction via simple features using shallow neural networks and decision trees, ACS Omega, vol.4, issue.1, pp.1874-1888, 2019.

A. Aliper, S. Plis, A. Artemov, and A. Ulloa, Polina Mamoshina, and Alex Zhavoronkov. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data, Molecular pharmaceutics, 2016.

B. Tyler, . Hughes, P. Grover, S. Miller, and . Swamidass, Modeling epoxidation of drug-like molecules with a deep machine learning network, ACS central science, vol.1, issue.4, pp.168-180, 2015.

R. Rodríguez-pérez, T. Miyao, S. Jasial, M. Vogt, and J. Bajorath, Prediction of compound profiling matrices using machine learning, ACS Omega, vol.3, issue.4, pp.4713-4723, 2018.

P. Hop, B. Allgood, and J. Yu, Geometric deep learning autonomously learns chemical features that outperform those engineered by domain experts, Molecular pharmaceutics, 2018.

Q. Feng, E. Dueva, A. Cherkasov, and M. Ester, Padme: A deep learning-based framework for drug-target interaction prediction, 2018.

C. Fare, L. Turcani, and E. , Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks, 2018.

A. Mayr, G. Klambauer, T. Unterthiner, M. Steijaert, K. Jörg et al., Djork-Arné Clevert, and Sepp Hochreiter. Large-scale comparison of machine learning methods for drug target prediction on chembl, Chemical Science, 2018.

H. Öztürk, A. Özgür, and E. Ozkirimli, Deepdta: deep drug-target binding affinity prediction, Bioinformatics, vol.34, issue.17, pp.821-829, 2018.

A. Kyle-yingkai-gao, H. Fokoue, A. Luo, S. Iyengar, P. Dey et al., Interpretable drug target prediction using deep neural representation, In IJCAI, pp.3371-3377, 2018.

M. Tsubaki, K. Tomii, and J. Sese, Compound-protein interaction prediction with endto-end learning of neural networks for graphs and sequences, Bioinformatics, 2018.

K. Xu, W. Hu, J. Leskovec, and S. Jegelka, , 2018.

C. Morris, M. Ritzert, M. Fey, L. William, J. E. Hamilton et al., Weisfeiler and leman go neural: Higher-order graph neural networks, 2018.

M. Hirohara, Y. Saito, Y. Koda, K. Sato, and Y. Sakakibara, Convolutional neural network based on smiles representation of compounds for detecting chemical motif, BMC bioinformatics, vol.19, issue.19, p.526, 2018.

S. Ruder, An overview of multi-task learning in deep neural networks, 2017.

A. Paul, D. Jha, R. Al-bahrani, W. Liao, A. Choudhary et al., Chemixnet: Mixed dnn architectures for predicting chemical properties using multiple molecular representations, 2018.

A. Paul, D. Jha, W. Liao, A. Choudhary, and A. Agrawal, Transfer learning using ensemble neural nets for organic solar cell screening, 2019.

Y. Ganin and V. Lempitsky, Unsupervised domain adaptation by backpropagation, 2014.

A. Tibo, M. Jaeger, and P. Frasconi, Learning and interpreting multi-multi-instance learning networks, 2018.

B. Jiang, Z. Zhang, J. Tang, and B. Luo, Multiple graph adversarial learning, 2019.

J. Zhang, B. Cao, S. Xie, C. Lu, P. S. Yu et al., Identifying connectivity patterns for brain diseases via multi-side-view guided deep architectures, Proceedings of the 2016 SIAM International Conference on Data Mining, pp.36-44, 2016.

Z. Zhang, Y. Zhao, X. Liao, W. Shi, K. Li et al., Deep learning in omics: a survey and guideline, Briefings in functional genomics, 2018.

S. Sun, A survey of multi-view machine learning, Neural Computing and Applications, vol.23, issue.7-8, pp.2031-2038, 2013.

P. Darrin, T. Lewis, W. S. Jebara, and . Noble, Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure, Bioinformatics, vol.22, issue.22, pp.2753-2760, 2006.

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 2006.

C. Azencott, Introduction to machine learning for bioinfoinformatics, 2018.

J. Davis and M. Goadrich, The relationship between precision-recall and roc curves, Proceedings of the 23rd international conference on Machine learning, pp.233-240, 2006.

A. Kashyap, What does an x axis represent in a logistic regression sigmoid function?, 2018.

J. Shawe, -. Taylor, and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.

J. Vert, Machine learning with kernel methods, 2015.

R. Caruana, Multitask learning. Machine learning, vol.28, pp.41-75, 1997.

R. Caruana and V. Sa, Promoting poor features to supervisors: Some inputs work better as outputs, Advances in Neural Information Processing Systems, pp.389-395, 1997.

H. Cheng, H. Fang, and M. Ostendorf, Open-domain name error detection using a multi-task rnn, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.737-746, 2015.

Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, Multi-task learning for classification with dirichlet process priors, Journal of Machine Learning Research, vol.8, pp.35-63, 2007.

V. Bellon, V. Stoven, and C. Azencott, Multitask feature selection with task descriptors, Biocomputing 2016: Proceedings of the Pacific Symposium, pp.261-272, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01246697

L. Jacob, J. Vert, and F. Bach, Clustered multi-task learning: A convex formulation, Advances in neural information processing systems, pp.745-752, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00320573

Y. Zhang, Y. Wei, and Q. Yang, Learning to multitask, 2018.

S. Ruder, J. Bingel, I. Augenstein, and A. Søgaard, Learning what to share between loosely related tasks, 2017.

A. Argyriou, T. Evgeniou, and M. Pontil, Multi-task feature learning, Advances in neural information processing systems, pp.41-48, 2007.

I. Misra, A. Shrivastava, A. Gupta, and M. Hebert, Cross-stitch networks for multi-task learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3994-4003, 2016.

C. Aurelie, G. Lozano, and . Swirszcz, Multi-level lasso for sparse multi-task regression, Proceedings of the 29th International Coference on International Conference on Machine Learning, pp.595-602, 2012.

A. Jalali, S. Sanghavi, C. Ruan, and P. Ravikumar, A dirty model for multi-task learning, Advances in neural information processing systems, pp.964-972, 2010.

P. Ting-kei-pong, S. Tseng, J. Ji, and . Ye, Trace norm regularization: Reformulations, algorithms, and multi-task learning, SIAM Journal on Optimization, vol.20, issue.6, pp.3465-3489, 2010.

L. Han and Y. Zhang, Learning tree structure in multi-task learning, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.397-406, 2015.

S. Kim and E. P. Xing, Tree-guided group lasso for multitask regression with structured sparsity, 2009.

X. Chen, S. Kim, Q. Lin, G. Jaime, E. P. Carbonell et al., Graph-structured multi-task regression and an efficient optimization method for general fused lasso, 2010.

G. Song and W. Chai, Collaborative learning for deep neural networks, Advances in Neural Information Processing Systems, pp.1837-1846, 2018.

. Tm-heskes, Empirical bayes for learning to learn, 2000.

K. Yu, A. Volker-tresp, and . Schwaighofer, Learning gaussian processes from multiple tasks, Proceedings of the 22nd international conference on Machine learning, pp.1012-1019, 2005.

H. Daumé and I. , Bayesian multitask learning with latent hierarchies, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp.135-142, 2009.

S. Thrun and J. Sullivan, Discovering structure in multiple learning tasks: The tc algorithm, ICML, vol.96, pp.489-497, 1996.

T. Evgeniou, A. Charles, M. Micchelli, and . Pontil, Learning multiple tasks with kernel methods, Journal of Machine Learning Research, vol.6, pp.615-637, 2005.

N. Geoffrey-e-hinton, A. Srivastava, I. Krizhevsky, R. Sutskever, and . Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, 2012.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010.

Y. Bengio, I. Goodfellow, and A. Courville, Deep learning. Book in preparation for, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01820431

A. Adriano, . Mol, S. Luiz, J. Martins-filho, S. Demisio-s-da et al., Efficiency parameters estimation in gemstones cut design using artificial neural networks, Computational materials science, vol.38, issue.4, pp.727-736, 2007.

L. Dormehl, What is an artificial neural network? here's everything you need to know

G. E. David-e-rumelhart, R. Hinton, and . Williams, Learning internal representations by error propagation, 1985.

G. Cybenko, Approximation by superpositions of a sigmoidal function, vol.2, pp.303-314, 1989.

K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural networks, vol.4, issue.2, pp.251-257, 1991.

, Christopher olah's blog on deep learning, pp.2018-2028

A. Graves, M. Abdel-rahman, and G. Hinton, Speech recognition with deep recurrent neural networks, Acoustics, speech and signal processing (icassp), 2013 ieee international conference on, pp.6645-6649, 2013.

O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, Show and tell: A neural image caption generator, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3156-3164, 2015.

P. Diederik, M. Kingma, and . Welling, Auto-encoding variational bayes, 2013.

P. Christopher, I. Burgess, A. Higgins, L. Pal, N. Matthey et al., Understanding disentangling in \ beta-vae, 2018.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford et al., Improved techniques for training gans, Advances in neural information processing systems, pp.2234-2242, 2016.

M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, International Conference on Machine Learning, pp.214-223, 2017.

I. Goodfellow, Tutorial: Generative adversarial networks, NIPS, 2016.

O. Vinyals and Q. Le, A neural conversational model, 2015.

D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, 2014.

W. Chan, N. Jaitly, Q. Le, and O. Vinyals, Listen, attend and spell: A neural network for large vocabulary conversational speech recognition, Acoustics, Speech and Signal Processing, pp.4960-4964, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Identity mappings in deep residual networks, European conference on computer vision, pp.630-645, 2016.

F. Wang, M. Jiang, C. Qian, S. Yang, C. Li et al., Residual attention network for image classification, 2017.

R. Caruna, Multitask learning: A knowledge-based source of inductive bias, Machine Learning: Proceedings of the Tenth International Conference, pp.41-48, 1993.

L. Duong, T. Cohn, S. Bird, and P. Cook, Low resource dependency parsing: Crosslingual parameter sharing in a neural network parser, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol.2, pp.845-850, 2015.

A. Rozantsev, M. Salzmann, and P. Fua, Beyond sharing weights for deep domain adaptation, 2018.

J. Sebastian-ruder12, I. Bingel, A. Augenstein, and . Søgaard, Sluice networks: Learning what to share between loosely related tasks, p.23, 2017.

L. Xiao, H. Zhang, W. Chen, Y. Wang, and Y. Jin, Learning what to share: Leaky multi-task network for text classification, Proceedings of the 27th International Conference on Computational Linguistics, pp.2055-2065, 2018.

S. Liu, E. Johns, and A. Davison, End-to-end multi-task learning with attention, 2018.

E. Meyerson and R. Miikkulainen, Beyond shared hierarchies: Deep multitask learning through soft layer ordering, 2017.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization, 2014.

H. Li, Z. Xu, G. Taylor, and T. Goldstein, Visualizing the loss landscape of neural nets, 2017.

Z. Frans-olaf, What methods do you prefer when performing hyperparameter optimization?

, What-methods-do-you-prefer-when-performing-hyperparameter-optimization

F. Hutter, H. Holger, K. Hoos, and . Leyton-brown, Sequential model-based optimization for general algorithm configuration, International Conference on Learning and Intelligent Optimization, pp.507-523, 2011.

J. Bergstra, D. David, and . Cox, Hyperparameter optimization and boosting for classifying facial expressions: How good can a" null, 2013.

J. Snoek, H. Larochelle, and R. Adams, Practical bayesian optimization of machine learning algorithms, Advances in neural information processing systems, pp.2951-2959, 2012.

B. Zoph, V. Quoc, and . Le, Neural architecture search with reinforcement learning, 2016.

T. Keith, . Butler, W. Daniel, H. Davies, O. Cartwright et al., Machine learning for molecular and materials science, Nature, vol.559, issue.7715, p.547, 2018.

P. Baldi, Deep learning in biomedical data science, Annual Review of Biomedical Data Science, vol.1, pp.181-205, 2018.

A. Zhavoronkov, Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry, 2018.

K. Preuer, G. Klambauer, and F. Rippmann, Sepp Hochreiter, and Thomas Unterthiner. Interpretable deep learning in drug discovery, 2019.

R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, Gnn explainer: A tool for post-hoc explanation of graph neural networks, 2019.

I. Cortés, -. Ciriano, and A. Bender, Deep confidence: A computationally efficient framework for calculating reliable prediction errors for deep neural networks, Journal of chemical information and modeling, 2018.

C. Olah, A. Mordvintsev, and L. Schubert, Feature visualization

D. Erhan, Y. Bengio, A. Courville, and P. Vincent, Visualizing higher-layer features of a deep network, vol.1341, p.1, 2009.

K. Simonyan, A. Vedaldi, and A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013.

D. Matthew, R. Zeiler, and . Fergus, Visualizing and understanding convolutional networks, European conference on computer vision, pp.818-833, 2014.

A. Ramprasaath-r-selvaraju, R. Das, M. Vedantam, D. Cogswell, D. Parikh et al., Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization, vol.7, 2016.

M. Sundararajan, A. Taly, and Q. Yan, Axiomatic attribution for deep networks, 2017.

P. Kindermans, T. Kristof, M. Schütt, K. Alber, S. Müller et al., Patternnet and patternlrp-improving the interpretability of neural networks, stat, vol.1050, p.16, 2017.

R. Sawada, H. Iwata, S. Mizutani, and Y. Yamanishi, Target-based drug repositioning using large-scale chemical-protein interactome data, Journal of Chemical Information and Modeling, vol.55, issue.12, pp.2717-2730, 2015.

R. G. Gert, N. Lanckriet, P. Cristianini, L. E. Bartlett, and M. Ghaoui, Learning the kernel matrix with semidefinite programming, The Journal of Machine Learning Research, vol.5, pp.27-72, 2004.

A. Rakotomamonjy, R. Francis, S. Bach, Y. Canu, and . Grandvalet, Simplemkl. Journal of Machine Learning Research, vol.9, pp.2491-2521, 2008.

M. Gönen and E. Alpayd?n, Multiple kernel learning algorithms, Journal of Machine Learning Research, vol.12, pp.2211-2268, 2011.

C. Cortes, M. Mohri, and A. Rostamizadeh, Learning non-linear combinations of kernels, Advances in neural information processing systems, pp.396-404, 2009.

B. Wang, M. Aziz, F. Mezlini, M. Demir, Z. Fiume et al., Similarity network fusion for aggregating data types on a genomic scale, Nature methods, vol.11, issue.3, pp.333-337, 2014.

M. Gönen, S. Khan, and S. Kaski, Kernelized bayesian matrix factorization, International Conference on Machine Learning, pp.864-872, 2013.

C. Kramer, B. Beck, and T. Clark, Insolubility classification with accurate prediction probabilities using a metaclassifier, Journal of chemical information and modeling, vol.50, issue.3, pp.404-414, 2010.

F. Cheng, Y. Yu, J. Shen, L. Yang, W. Li et al., Classification of cytochrome p450 inhibitors and noninhibitors using combined classifiers, Journal of chemical information and modeling, vol.51, issue.5, pp.996-1011, 2011.

P. Darrin, T. Lewis, W. S. Jebara, and . Noble, Nonstationary kernel combination, Proceedings of the 23rd international conference on Machine learning, pp.553-560, 2006.

S. Cao, W. Lu, and Q. Xu, Grarep: Learning graph representations with global structural information, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp.891-900, 2015.

M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu, Asymmetric transitivity preserving graph embedding, Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1105-1114, 2016.

A. Ahmed, N. Shervashidze, S. Narayanamurthy, V. Josifovski, and A. J. Smola, Distributed large-scale natural graph factorization, Proceedings of the 22nd international conference on World Wide Web, pp.37-48, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00918478

M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in neural information processing systems, pp.585-591, 2002.

M. Niepert, M. Ahmed, and K. Kutzkov, Learning convolutional neural networks for graphs, International conference on machine learning, pp.2014-2023, 2016.

G. Urban, N. Subrahmanya, and P. Baldi, Inner and outer recursive neural networks for chemoinformatics applications, Journal of chemical information and modeling, vol.58, issue.2, pp.207-211, 2018.

B. Garrett, C. Goh, A. Siegel, . Vishnu, O. Nathan et al., Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed qsar/qspr models, 2017.

T. Lei, W. Jin, R. Barzilay, and T. Jaakkola, Deriving neural architectures from sequence and graph kernels, 2017.

R. Gómez-bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-lobato, B. Sánchez-lengeling et al., Automatic chemical design using a data-driven continuous representation of molecules, ACS central science, vol.4, issue.2, pp.268-276, 2018.

R. Griffiths, Constrained bayesian optimization for automatic chemical design, 2017.

J. Matt, B. Kusner, and J. Paige, Grammar variational autoencoder, 2017.

N. De, C. , and T. Kipf, Molgan: An implicit generative model for small molecular graphs, 2018.

J. You, R. Ying, X. Ren, L. William, J. Hamilton et al., Graphrnn: A deep generative model for graphs, 2018.

M. Simonovsky and N. Komodakis, Graphvae: Towards generation of small graphs using variational autoencoders, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01990381

D. Daniel and . Johnson, Learning graphical state transitions, 2016.

Y. Li, O. Vinyals, C. Dyer, R. Pascanu, and P. Battaglia, Learning deep generative models of graphs, 2018.

W. Jin, K. Yang, R. Barzilay, and T. Jaakkola, Learning multimodal graph-to-graph translation for molecular optimization, 2018.

N. Brown, M. Fiscato, H. S. Marwin, A. C. Segler, and . Vaucher, Guacamol: Benchmarking models for de novo molecular design, 2018.

Q. Liu, M. Allamanis, M. Brockschmidt, and A. L. Gaunt, Constrained graph variational autoencoders for molecule design, 2018.

J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec, Graph convolutional policy network for goal-directed molecular graph generation, 2018.

G. Schneider and U. Fechner, Computer-based de novo design of drug-like molecules, Nature Reviews Drug Discovery, vol.4, issue.8, p.649, 2005.

C. Rupakheti, A. Virshup, W. Yang, and D. N. Beratan, Strategy to discover diverse optimal molecules in the small molecule universe, Journal of chemical information and modeling, vol.55, issue.3, pp.529-537, 2015.

A. Kadurin, A. Aliper, A. Kazennov, P. Mamoshina, Q. Vanhaelen et al., The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology, Oncotarget, vol.8, issue.7, p.10883, 2017.

A. Kadurin, S. Nikolenko, K. Khrabrov, A. Aliper, and A. Zhavoronkov, drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico, Molecular pharmaceutics, vol.14, issue.9, pp.3098-3104, 2017.

X. Yang, J. Zhang, K. Yoshizoe, K. Terayama, and K. Tsuda, Chemts: an efficient python library for de novo molecular generation, Science and technology of advanced materials, vol.18, issue.1, pp.972-976, 2017.

M. Olivecrona, T. Blaschke, O. Engkvist, and H. Chen, Molecular de-novo design through deep reinforcement learning, Journal of cheminformatics, vol.9, issue.1, p.48, 2017.

M. Popova, O. Isayev, and A. Tropsha, Deep reinforcement learning for de novo drug design, Science advances, vol.4, issue.7, p.7885, 2018.

L. Yu, W. Zhang, J. Wang, and Y. Yu, Seqgan: Sequence generative adversarial nets with policy gradient, Thirty-First AAAI Conference on Artificial Intelligence, 2017.

B. Gabriel-lima-guimaraes, C. Sanchez-lengeling, P. Outeiral, A. Farias, and . Aspuru-guzik, Objective-reinforced generative adversarial networks (organ) for sequence generation models, 2017.

B. Sanchez-lengeling, C. Outeiral, L. Gabriel, A. Guimaraes, and . Aspuru-guzik, Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic, 2017.

E. Putin, A. Asadulaev, Y. Ivanenkov, V. Aladinskiy, B. Sanchez-lengeling et al., Reinforced adversarial neural computer for de novo molecular design, Journal of chemical information and modeling, vol.58, issue.6, pp.1194-1204, 2018.

W. Jin, R. Barzilay, and T. Jaakkola, Junction tree variational autoencoder for molecular graph generation, 2018.

R. Assouel, M. Ahmed, H. Marwin, A. Segler, Y. Saffari et al., Defactor: Differentiable edge factorization-based probabilistic graph generation, 2018.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, Improved training of wasserstein gans, Advances in Neural Information Processing Systems, pp.5767-5777, 2017.

H. S. Marwin, T. Segler, C. Kogej, M. P. Tyrchan, and . Waller, Generating focused molecule libraries for drug discovery with recurrent neural networks, ACS central science, vol.4, issue.1, pp.120-131, 2017.

D. Polykovskiy, A. Zhebrak, D. Vetrov, Y. Ivanenkov, V. Aladinskiy et al., Entangled conditional adversarial autoencoder for de novo drug discovery, Molecular pharmaceutics, vol.15, issue.10, pp.4398-4405, 2018.

A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, , 2015.

B. Cameron, E. Browne, D. Powley, . Whitehouse, M. Simon et al., A survey of monte carlo tree search methods, IEEE Transactions on Computational Intelligence and AI in games, vol.4, issue.1, pp.1-43, 2012.

J. Ronald and . Williams, Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine learning, vol.8, issue.3-4, pp.229-256, 1992.

E. Putin, A. Asadulaev, Q. Vanhaelen, Y. Ivanenkov, A. V. Aladinskaya et al., Adversarial threshold neural computer for molecular de novo design, Molecular pharmaceutics, vol.15, issue.10, pp.4386-4397, 2018.

P. Timothy, J. J. Lillicrap, A. Hunt, N. Pritzel, T. Heess et al., Continuous control with deep reinforcement learning, 2015.

D. Polykovskiy, A. Zhebrak, B. Sanchez-lengeling, S. Golovanov, O. Tatanov et al., Molecular sets (moses): A benchmarking platform for molecular generation models, 2018.

K. Preuer, P. Renz, T. Unterthiner, S. Hochreiter, and G. Klambauer, Fréchet chemnet distance: a metric for generative models for molecules in drug discovery, Journal of chemical information and modeling, vol.58, issue.9, pp.1736-1741, 2018.

. G-richard-bickerton, V. Gaia, J. Paolini, S. Besnard, A. Muresan et al., Quantifying the chemical beauty of drugs, Nature chemistry, vol.4, issue.2, p.90, 2012.

P. Ertl and A. Schuffenhauer, Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions, Journal of cheminformatics, vol.1, issue.1, 2009.

S. Michael, . Lajiness, M. Gerald, V. Maggiora, and . Shanmugasundaram, Assessment of the consistency of medicinal chemists in reviewing sets of compounds, Journal of medicinal chemistry, vol.47, issue.20, pp.4891-4896, 2004.