X. Jalencas and J. Mestres, On the origins of drug polypharmacology, Med. Chem. Commun., vol.472, issue.suppl. 1, p.80, 2013.
DOI : 10.1039/C2MD20242E

E. Lounkine, M. Keiser, S. Whitebread, D. Mikhailov, J. Hamon et al., Large-scale prediction and testing of drug activity on side-effect targets, Nature, vol.57, pp.361-368, 2012.
DOI : 10.1038/nature11159

I. Cortes-ciriano, Q. Ain, V. Subramanian, E. Lenselink, O. Mendez-lucio et al., Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects, Med. Chem. Commun., vol.503, issue.suppl. 1, pp.24-50, 2015.
DOI : 10.1093/bib/bbu010

G. Van-westen, R. Swier, I. Cortes-ciriano, J. Wegner, J. Overington et al., Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets, Journal of Cheminformatics, vol.5, issue.1, p.42, 2013.
DOI : 10.1093/bioinformatics/16.5.412

V. Poroikov, D. Filimonov, A. Lagunin, T. Gloriozova, and A. Zakharov, PASS: identification of probable targets and mechanisms of toxicity???, SAR and QSAR in Environmental Research, vol.43, issue.1-2, pp.101-111, 2007.
DOI : 10.1016/S1383-5742(03)00051-6

. Nidhi, M. Glick, J. Davies, and J. Jenkins, Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases, Journal of Chemical Information and Modeling, vol.46, issue.3, pp.1124-1157, 2006.
DOI : 10.1021/ci060003g

F. Nigsch, A. Bender, J. Jenkins, and J. Mitchell, Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics, Journal of Chemical Information and Modeling, vol.48, issue.12, pp.2313-2338, 2008.
DOI : 10.1021/ci800079x

M. Keiser, B. Roth, B. Armbruster, P. Ernsberger, J. Irwin et al., Relating protein pharmacology by ligand chemistry, Nature Biotechnology, vol.271, issue.2, pp.197-206, 2007.
DOI : 10.1038/nbt1284

N. Wale and G. Karypis, Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods, Journal of Chemical Information and Modeling, vol.49, issue.10, pp.2190-201, 2010.
DOI : 10.1021/ci9000376

A. Koutsoukas, R. Lowe, Y. Kalantarmotamedi, H. Mussa, W. Klaffke et al., In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Na??ve Bayes and Parzen-Rosenblatt Window, Journal of Chemical Information and Modeling, vol.53, issue.8, pp.1957-66, 2013.
DOI : 10.1021/ci300435j

G. Van-westen, J. Wegner, A. Ijzerman, H. Van-vlijmen, and A. Bender, Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets, Med. Chem. Commun., vol.48, issue.1, pp.16-30, 2011.
DOI : 10.1039/C0MD00165A

P. Perlmann and M. Troye-blomberg, Malaria blood-stage infection and its conyrol by the immune system, Folia Biol, vol.46, pp.210-218, 2000.

P. Olliaro, Mode of action and mechanisms of resistance for antimalarial drugs, Pharmacology & Therapeutics, vol.89, issue.2, pp.207-226, 2001.
DOI : 10.1016/S0163-7258(00)00115-7

D. Hecht and G. Fogel, Modeling the evolution of drug resistance in malaria, Journal of Computer-Aided Molecular Design, vol.49, issue.4, pp.1343-53, 2012.
DOI : 10.1007/s10822-012-9618-2

M. Moran, J. Guzman, and A. Ropars, The malaria product pipeline: planning for the future. In: The George Institute for International Health, 2007.

F. Gamo, L. Sanz, J. Vidal, C. De-cozar, E. Alvarez et al., Thousands of chemical starting points for antimalarial lead identification, Nature, vol.8, issue.7296, pp.305-315, 2010.
DOI : 10.1038/nature09107

R. Verma, A. Tiwari, S. Kaur, G. Varshney, and G. Raghava, Identification of Proteins Secreted by Malaria Parasite into Erythrocyte using SVM and PSSM profiles, BMC Bioinformatics, vol.9, issue.1, p.201, 2008.
DOI : 10.1186/1471-2105-9-201

S. Jamal, V. Periwal, and V. Scaria, Predictive modeling of anti-malarial molecules inhibiting apicoplast formation, BMC Bioinformatics, vol.14, issue.1, pp.2105-2119, 2013.
DOI : 10.1023/A:1010933404324

S. Subramaniam, M. Mehrotra, and D. Gupta, Support Vector Machine Based Prediction of P. falciparum Proteasome Inhibitors and Development of Focused Library by Molecular Docking, Combinatorial Chemistry & High Throughput Screening, vol.14, issue.10, pp.898-907, 2011.
DOI : 10.2174/138620711797537058

A. Gaulton, L. Bellis, A. Bento, J. Chambers, M. Davies et al., ChEMBL: a large-scale bioactivity database for drug discovery, Nucleic Acids Research, vol.40, issue.D1, pp.1100-1107, 2012.
DOI : 10.1093/nar/gkr777

A. Bender, Databases: Compound bioactivities go public, Nature Chemical Biology, vol.6, issue.5, p.309, 2010.
DOI : 10.1038/nchembio.354

S. Hunter, P. Jones, A. Mitchell, R. Apweiler, T. Attwood et al., InterPro in 2011: new developments in the family and domain prediction database, Nucleic Acids Research, vol.40, issue.D1, pp.306-318, 2011.
DOI : 10.1093/nar/gkr948
URL : https://hal.archives-ouvertes.fr/hal-00697960

A. Bender, H. Mussa, and R. Glen, Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Na??ve Bayesian Classifier, Journal of Chemical Information and Computer Sciences, vol.44, issue.1, pp.170-178, 2004.
DOI : 10.1021/ci034207y

A. Bender, H. Mussa, and R. Glen, Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D):??? Evaluation of Performance, Journal of Chemical Information and Computer Sciences, vol.44, issue.5, pp.1708-1726, 2004.
DOI : 10.1021/ci0498719

M. Sastry, J. Lowrie, S. Dixon, and W. Sherman, Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments, Journal of Chemical Information and Modeling, vol.50, issue.5, pp.771-84, 2010.
DOI : 10.1021/ci100062n

O. Boyle, N. Morley, C. Hutchison, and G. , Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit, Chemistry Central Journal, vol.2, issue.1, pp.5-11, 2008.
DOI : 10.1186/1752-153X-2-5

T. Crisman, C. Parker, J. Jenkins, J. Scheiber, M. Thoma et al., Understanding False Positives in Reporter Gene Assays:?? in Silico Chemogenomics Approaches To Prioritize Cell-Based HTS Data, Journal of Chemical Information and Modeling, vol.47, issue.4, pp.1319-1346, 2007.
DOI : 10.1021/ci6005504

A. Bender, D. Mikhailov, M. Glick, J. Scheiber, J. Davies et al., Use of Ligand Based Models for Protein Domains To Predict Novel Molecular Targets and Applications To Triage Affinity Chromatography Data, Journal of Proteome Research, vol.8, issue.5, pp.2575-85, 2009.
DOI : 10.1021/pr900107z

P. Prathipati, N. Ma, U. Manjunatha, and A. Bender, Target Deconvolution Model Development and Validation, Journal of Proteome Research, vol.8, issue.6, pp.2788-98, 2009.
DOI : 10.1021/pr8010843

D. Murrell, I. Cortes-ciriano, G. Van-westen, I. Stott, T. Malliavin et al., Chemistry Aware Model Builder (camb): an R Package for Predictive Bioactivity Modeling, 2014.

F. Sievers, A. Wilm, D. Dineen, T. Gibson, K. Karplus et al., Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega, Molecular Systems Biology, vol.7, issue.1, p.539, 2011.
DOI : 10.1093/nar/gkn174

V. Consonni, D. Ballabio, and R. Todeschini, Evaluation of model predictive ability by external validation techniques, Journal of Chemometrics, vol.41, issue.3-4, pp.194-201, 2010.
DOI : 10.1002/cem.1290

D. Hawkins, S. Basak, and D. Mills, Assessing Model Fit by Cross-Validation, Journal of Chemical Information and Computer Sciences, vol.43, issue.2
DOI : 10.1021/ci025626i

A. Tropsha, P. Gramatica, and V. Gombar, The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models, QSAR & Combinatorial Science, vol.38, issue.1, pp.69-77, 2003.
DOI : 10.1002/qsar.200390007

A. Golbraikh and A. Tropsha, Beware of q2!, Journal of Molecular Graphics and Modelling, vol.20, issue.4, pp.269-76, 2002.
DOI : 10.1016/S1093-3263(01)00123-1

J. Brown, Y. Okuno, G. Marcou, A. Varnek, and D. Horvath, Computational chemogenomics: Is it more than inductive transfer?, Journal of Computer-Aided Molecular Design, vol.2, issue.1, pp.597-618, 2014.
DOI : 10.1007/s10822-014-9743-1

A. Ben-hur and C. Ong, Support Vector Machines and Kernels for Computational Biology, PLoS Computational Biology, vol.14, issue.10, p.1000173, 2008.
DOI : 10.1371/journal.pcbi.1000173.t002

J. Friedman, machine., The Annals of Statistics, vol.29, issue.5, pp.1189-232, 2001.
DOI : 10.1214/aos/1013203451

C. Rasmussen and C. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

M. Kuhn, Building Predictive Models in R Using the caret Package, J Stat Softw, vol.28, pp.1-26, 2008.

A. Spitzmüller and J. Mestres, Prediction of the P. falciparum Target Space Relevant to Malaria Drug Discovery, PLoS Computational Biology, vol.37, issue.10, p.1003257, 2013.
DOI : 10.1371/journal.pcbi.1003257.s003

F. Martínez-jiménez, G. Papadatos, L. Yang, I. Wallace, V. Kumar et al., Target Prediction for an Open Access Set of Compounds Active against Mycobacterium tuberculosis, PLoS Computational Biology, vol.30, issue.4, p.1003253, 2013.
DOI : 10.1371/journal.pcbi.1003253.s003

R. Clark and P. Fox, Statistical variation in progressive scrambling, Journal of Computer-Aided Molecular Design, vol.7, issue.7-9, pp.563-76, 2004.
DOI : 10.1007/s10822-004-4077-z

Y. Yuthavong, B. Tarnchompoo, T. Vilaivan, P. Chitnumsub, S. Kamchonwongpaisan et al., Malarial dihydrofolate reductase as a paradigm for drug development against a resistance-compromised target, Proceedings of the National Academy of Sciences, vol.109, issue.42, pp.16823-16831, 2012.
DOI : 10.1073/pnas.1204556109

K. Ersmark, B. Samuelsson, and A. Hallberg, Plasmepsins as potential targets for new antimalarial therapy, Medicinal Research Reviews, vol.2, issue.5, pp.626-66, 2006.
DOI : 10.1002/med.20082

M. Marco and J. Coterón, Falcipain Inhibition as a Promising Antimalarial Target, Current Topics in Medicinal Chemistry, vol.12, issue.5, pp.408-452, 2012.
DOI : 10.2174/156802612799362913

K. Andrews, T. Tran, N. Wheatley, and D. Fairlie, Targeting Histone Deacetylase Inhibitors for Anti-Malarial Therapy, Current Topics in Medicinal Chemistry, vol.9, issue.3, pp.292-308, 2009.
DOI : 10.2174/156802609788085313

I. Cortes-ciriano, A. Koutsoukas, O. Abian, R. Glen, A. Velazquez-campoy et al., Experimental validation of in silico target predictions on synergistic protein targets, Med. Chem. Commun., vol.15, issue.1, pp.278-88, 2013.
DOI : 10.1039/C2MD20286G