A. Jabeen and S. Ranganathan, Applications of machine learning in GPCR bioactive ligand discovery, Curr. Opin. Struct. Biol, vol.55, pp.66-76, 2019.

C. N. Cavasotto and A. J. Orry, Ligand Docking and Structure-based Virtual Screening in Drug Discovery, Curr. Top. Med. Chem, vol.7, pp.1015-1023, 2007.

D. L. Ma, D. S. Chan, and C. H. Leung, Drug repositioning by structure-based virtual screening, Chem. Soc. Rev, vol.42, pp.2130-2141, 2013.

S. Kar and K. Roy, How far can virtual screening take us in drug discovery?, Expert Opin. Drug Discov, vol.8, pp.245-261, 2013.

B. Gautier, M. A. Miteva, V. Goncalves, F. Huguenot, P. Coric et al., Targeting the proangiogenic VEGF-VEGFR protein-protein interface with drug-like compounds by in silico and in vitro screening, Chem. Biol, vol.18, pp.1631-1639, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01061760

F. Chevillard, D. Lagorce, C. Reynes, B. O. Villoutreix, P. Vayer et al., In silico prediction of aqueous solubility: A multimodel protocol based on chemical similarity, Mol. Pharm, vol.9, pp.3127-3135, 2012.

G. Moroy, O. Sperandio, S. Rielland, S. Khemka, K. Druart et al., Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis, Future Med. Chem, vol.7, pp.2317-2331, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01274987

H. Li, J. Peng, P. Sidorov, Y. Leung, K. S. Leung et al., Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data, Bioinformatics, 2019.

T. Scior, A. Bender, G. Tresadern, J. L. Medina-franco, K. Martinez-mayorga et al., Recognizing pitfalls in virtual screening: A critical review, J. Chem. Inf. Modeling, vol.52, pp.867-881, 2012.

Q. U. Ain, A. Aleksandrova, F. D. Roessler, and P. J. Ballester, Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening, Wiley Interdiscip. Rev. Comput. Mol. Sci, vol.5, pp.405-424, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01787236

E. Yuriev, J. Holien, and P. A. Ramsland, Improvements, trends, and new ideas in molecular docking: 2012-2013 in review, J. Mol. Recognit, vol.28, pp.581-604, 2015.

D. Douguet, A computational-aided drug design web server, Nucleic Acids Res, vol.38, pp.615-621, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00497221

A. Grosdidier, V. Zoete, and O. Michielin, SwissDock, a protein-small molecule docking web service based on EADock DSS, Nucleic Acids Res, vol.39, pp.270-277, 2011.

X. Ouyang, S. Zhou, Z. Ge, R. Li, and C. K. Kwoh, CovalentDock Cloud: A web server for automated covalent docking, Nucleic Acids Res, vol.41, pp.329-332, 2013.

D. E. Pires and D. B. Ascher, CSM-lig: A web server for assessing and comparing protein-small molecule affinities, Nucleic Acids Res, vol.44, pp.557-561, 2016.

T. Y. Tsai, K. W. Chang, C. Y. Chen, and . Iscreen, World's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan, J. Comput.-Aided Mol. Des, vol.25, pp.525-531, 2011.

J. J. Irwin, B. K. Shoichet, M. M. Mysinger, N. Huang, F. Colizzi et al., Automated docking screens: A feasibility study, J. Med. Chem, vol.52, pp.5712-5720, 2009.

H. Li, K. S. Leung, M. H. Wong, and P. J. Ballester, USR-VS: A web server for large-scale prospective virtual screening using ultrafast shape recognition techniques, Nucleic Acids Res, vol.44, pp.436-441, 2016.

D. Lagorce, L. Bouslama, J. Becot, M. A. Miteva, B. O. Villoutreix et al., Free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinform. (Oxf. Engl, vol.33, pp.3658-3660, 2017.

C. M. Labbe, J. Rey, D. Lagorce, M. Vavrusa, J. Becot et al., A web server for structure-based virtual screening, Nucleic Acids Res, vol.43, pp.448-454, 2015.

C. M. Labbe, T. Pencheva, D. Jereva, D. Desvillechabrol, J. Becot et al., AMMOS2: A web server for protein-ligand-water complexes refinement via molecular mechanics, Nucleic Acids Res, vol.45, pp.350-355, 2017.

C. Alland, F. Moreews, D. Boens, M. Carpentier, S. Chiusa et al., RPBS: A web resource for structural bioinformatics, Nucleic Acids Res, vol.33, pp.44-49, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00180478

J. B. Baell and G. A. Holloway, New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays, J. Med. Chem, vol.53, pp.2719-2740, 2010.

M. A. Miteva, S. Violas, M. Montes, D. Gomez, P. Tuffery et al., FAF-Drugs: Free ADME/tox filtering of compound collections, Nucleic Acids Res, vol.34, pp.738-744, 2006.
URL : https://hal.archives-ouvertes.fr/inserm-00106930

J. D. Hughes, J. Blagg, D. A. Price, S. Bailey, G. A. Decrescenzo et al., Physiochemical drug properties associated with in vivo toxicological outcomes, Bioorg. Med. Chem. Lett, vol.18, pp.4872-4875, 2008.

M. P. Gleeson, Generation of a set of simple, interpretable ADMET rules of thumb, J. Med. Chem, vol.51, pp.817-834, 2008.

K. R. Przybylak, A. R. Alzahrani, and M. T. Cronin, How does the quality of phospholipidosis data influence the predictivity of structural alerts?, J. Chem. Inf. Modeling, vol.54, pp.2224-2232, 2014.

R. F. Bruns and I. A. Watson, Rules for identifying potentially reactive or promiscuous compounds, J. Med. Chem, vol.55, pp.9763-9772, 2012.

M. A. Miteva, F. Guyon, and P. Tuffery, Frog2: Efficient 3D conformation ensemble generator for small compounds, Nucleic Acids Res, vol.38, pp.622-627, 2010.

G. M. Morris, R. Huey, W. Lindstrom, M. F. Sanner, R. K. Belew et al., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, J. Comput. Chem, vol.30, pp.2785-2791, 2009.

O. Trott and A. J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem, pp.455-461, 2010.

N. Lagarde, J. Rey, A. Gyulkhandanyan, P. Tuffery, M. A. Miteva et al., Online structure-based screening of purchasable approved drugs and natural compounds: Retrospective examples of drug repositioning on cancer targets, Oncotarget, vol.9, pp.32346-32361, 2018.

A. Mullard, Protein-protein interaction inhibitors get into the groove, Nat. Rev. Drug Discov, vol.11, pp.173-175, 2012.

X. Zhang, S. Betzi, X. Morelli, and P. Roche, Focused chemical libraries-design and enrichment: An example of protein-protein interaction chemical space, Future Med. Chem, vol.6, pp.1291-1307, 2014.

B. O. Villoutreix, M. A. Kuenemann, J. L. Poyet, H. Bruzzoni-giovanelli, C. Labbe et al., Drug-like protein-protein interaction modulators: Challenges and opportunities for drug discovery and chemical biology, Mol. Inform, vol.33, pp.414-437, 2014.

I. T. Weber and R. W. Harrison, Molecular mechanics calculations on Rous sarcoma virus protease with peptide substrates, Protein Sci.: A Publ. Protein Soc, vol.6, pp.2365-2374, 1997.

T. Pencheva, D. Lagorce, I. Pajeva, B. O. Villoutreix, M. A. Miteva et al., Automated Molecular Mechanics Optimization tool for in silico Screening, BMC Bioinform, vol.9, 2008.
URL : https://hal.archives-ouvertes.fr/inserm-00668481

P. A. Janssen, C. J. Niemegeers, K. H. Schellekens, F. M. Lenaerts, F. J. Verbruggen et al., The pharmacology of penfluridol (R 16341) a new potent and orally long-acting neuroleptic drug, Eur. J. Pharmacol, vol.11, pp.139-154, 1970.

P. Hassel, Experimental comparison of low doses of 1.5 mg fluspirilene and bromazepam in out-patients with psychovegetative disturbances, Pharmacopsychiatry, vol.18, pp.297-302, 1985.

S. J. Wang, Inhibition of glutamate release by fluspirilene in cerebrocortical nerve terminals (synaptosomes), Synapse, vol.44, pp.36-41, 2002.

X. N. Shi, H. Li, H. Yao, X. Liu, L. Li et al., In Silico Identification and In Vitro and In Vivo Validation of Anti-Psychotic Drug Fluspirilene as a Potential CDK2 Inhibitor and a Candidate Anti-Cancer Drug, PLoS ONE, vol.10, 2015.

U. Asghar, A. K. Witkiewicz, N. C. Turner, and E. S. Knudsen, The history and future of targeting cyclin-dependent kinases in cancer therapy, Nat. Rev. Drug Discov, vol.14, pp.130-146, 2015.

A. Cranney and J. D. Adachi, Benefit-risk assessment of raloxifene in postmenopausal osteoporosis, Drug Saf, vol.28, pp.721-730, 2005.

H. U. Bryant, Mechanism of action and preclinical profile of raloxifene, a selective estrogen receptor modulation, Rev. Endocr. Metab. Disord, vol.2, pp.129-138, 2001.

H. Li, H. Xiao, L. Lin, D. Jou, V. Kumari et al., Drug design targeting protein-protein interactions (PPIs) using multiple ligand simultaneous docking (MLSD) and drug repositioning: Discovery of raloxifene and bazedoxifene as novel inhibitors of IL-6/GP130 interface, J. Med. Chem, vol.57, pp.632-641, 2014.

D. E. Johnson, R. A. O'keefe, and J. R. Grandis, Targeting the IL-6/JAK/STAT3 signalling axis in cancer, Nat. Rev. Clin. Oncol, vol.15, pp.234-248, 2018.

N. Vargesson, Thalidomide-induced teratogenesis: History and mechanisms, Birth Defects Res. Part C Embryo Today Rev, vol.105, pp.140-156, 2015.

S. Singhal, J. Mehta, R. Desikan, D. Ayers, P. Roberson et al., Antitumor activity of thalidomide in refractory multiple myeloma, New Engl. J. Med, vol.341, pp.1565-1571, 1999.

A. K. Stewart and . Medicine, How thalidomide works against cancer, vol.343, pp.256-257, 2014.

N. M. O'boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch et al., Open Babel: An open chemical toolbox, J. Cheminformatics, vol.3, 2011.

C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev, vol.46, pp.3-26, 2001.

M. Congreve, R. Carr, C. Murray, and H. Jhoti, A 'rule of three' for fragment-based lead discovery?, Drug Discov. Today, vol.8, pp.876-877, 2003.

P. Workman and I. Collins, Probing the probes: Fitness factors for small molecule tools, Chem. Biol, vol.17, pp.561-577, 2010.

P. S. Charifson and W. P. Walters, Filtering databases and chemical libraries, Mol. Divers, vol.5, pp.185-197, 2002.

J. J. Irwin and B. K. Shoichet, ZINC-a free database of commercially available compounds for virtual screening, J. Chem. Inf. Modeling, vol.45, pp.177-182, 2005.

P. Jeffrey and S. Summerfield, Assessment of the blood-brain barrier in CNS drug discovery, Neurobiol. Dis, vol.37, pp.33-37, 2010.

T. J. Ritchie, C. N. Luscombe, and S. J. Macdonald, Analysis of the calculated physicochemical properties of respiratory drugs: Can we design for inhaled drugs yet?, J. Chem. Inf. Modeling, vol.49, pp.1025-1032, 2009.

E. Pihan, L. Colliandre, J. F. Guichou, and D. Douguet, 3D structure collections dedicated to drug repurposing and fragment-based drug design. Bioinform, vol.28, pp.1540-1541, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02115767

M. Biasini and . Zenodo, pv: v1.8.1 (Version V1.8.1), p.18, 2015.

A. K. Rappé, C. J. Casewit, K. S. Colwell, I. Goddard, W. A. Skiff et al., UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations, J. Am. Chem. Soc, vol.114, pp.10024-10035, 1992.

S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. Case, An all atom force field for simulations of proteins and nucleic acids, J. Comput. Chem, vol.7, pp.230-252, 1986.

S. Salentin, S. Schreiber, V. J. Haupt, M. F. Adasme, and M. Schroeder, PLIP: Fully automated protein-ligand interaction profiler, Nucleic Acids Res, vol.43, pp.443-447, 2015.

A. Gaulton, L. J. Bellis, A. P. Bento, J. Chambers, M. Davies et al., ChEMBL: A large-scale bioactivity database for drug discovery, Nucleic Acids Res, vol.40, pp.1100-1107, 2012.

D. S. Wishart, Y. D. Feunang, A. C. Guo, E. J. Lo, A. Marcu et al., Nucleic Acids Res, vol.46, pp.1074-1082, 2018.

O. Ursu, J. Holmes, J. Knockel, C. G. Bologa, J. J. Yang et al., Online drug compendium, vol.45, pp.932-939, 2017.

V. B. Siramshetty, O. A. Eckert, B. O. Gohlke, A. Goede, Q. Chen et al., SuperDRUG2: A one stop resource for approved/marketed drugs, Nucleic Acids Res, vol.46, pp.1137-1143, 2018.

D. Lagorce, O. Sperandio, J. B. Baell, M. A. Miteva, and B. O. Villoutreix, FAF-Drugs3: A web server for compound property calculation and chemical library design, Nucleic Acids Res, vol.43, pp.200-207, 2015.

T. Sterling and J. J. Irwin, ZINC 15-Ligand Discovery for Everyone, J. Chem. Inf. Modeling, vol.55, pp.2324-2337, 2015.

. Chemaxon, Available online: www.chemaxon.com (accessed on 18, vol.23, 2019.

C. 3d-structure-generator and . Classic, Available online: www.mn-am.com (accessed on 18, Molecular Networks GmbH, 2019.

E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt et al., UCSF Chimera-a visualization system for exploratory research and analysis, J. Comput. Chem, vol.25, pp.1605-1612, 2004.

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