R. L. Thurmond, S. Sun, L. Karlsson, and J. P. Edwards, Cathepsin S inhibitors as novel immunomodulators. Current opinion in investigational drugs, vol.6, pp.473-482, 2000.

J. O. Link and S. Zipfel, Advances in cathepsin S inhibitor design. Current opinion in drug discovery & development, vol.9, pp.471-482, 2006.

J. J. Wiener, S. Sun, and R. L. Thurmond, Recent advances in the design of cathepsin S inhibitors, Current topics in medicinal chemistry, vol.10, issue.7, pp.717-732, 2010.

A. Lee-dutra, D. K. Wiener, and S. Sun, Cathepsin S inhibitors, Expert Opin Ther Pat, vol.21, issue.3, pp.311-337, 2004.

R. D. Wilkinson, R. Williams, C. J. Scott, and R. E. Burden, Cathepsin S: therapeutic, diagnostic, and prognostic potential, Biological chemistry, vol.396, issue.8, pp.867-882, 2015.

H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat et al., The Protein Data Bank, Nucleic Acids Res, vol.28, issue.1, pp.235-242, 2000.

M. L. Verdonk, J. C. Cole, M. J. Hartshorn, C. W. Murray, and R. D. Taylor, Improved proteinligand docking using GOLD, Proteins Struct Funct Bioinf, vol.52, issue.4, pp.609-623, 2003.

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, vol.31, issue.2, pp.455-461, 2010.

E. Selwa, E. Elisée, A. Zavala, and B. I. Iorga, Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations, J Comput Aided Mol Des, vol.32, issue.1, pp.273-286, 2018.

S. Pronk, S. Pall, R. Schulz, P. Larsson, P. Bjelkmar et al., GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics, vol.29, issue.7, pp.845-854, 2013.

G. A. Kaminski, R. A. Friesner, J. Tirado-rives, and W. L. Jorgensen, Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides, J Phys Chem B, vol.105, issue.28, pp.6474-6487, 2001.

M. J. Robertson, J. Tirado-rives, and W. L. Jorgensen, Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field, J Chem Theory Comput, vol.11, issue.7, pp.3499-3509, 2015.

V. Gapsys, S. Michielssens, D. Seeliger, and B. L. De-groot, pmx: Automated protein structure and topology generation for alchemical perturbations, J Comput Chem, vol.36, issue.5, pp.348-354, 2015.

V. Gapsys, S. Michielssens, J. H. Peters, B. L. De-groot, and H. Leonov, Calculation of binding free energies, Methods in molecular biology, vol.1215, pp.173-209, 2015.

V. Gapsys, S. Michielssens, D. Seeliger, and B. L. De-groot, Accurate and Rigorous Prediction of the Changes in Protein Free Energies in a Large-Scale Mutation Scan, Angewandte Chemie, vol.55, pp.7364-7368, 2016.

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, issue.13, pp.1605-1612, 2004.

G. Surpateanu and B. I. Iorga, Evaluation of docking performance in a blinded virtual screening of fragment-like trypsin inhibitors, J Comput Aided Mol Des, vol.26, issue.5, pp.595-601, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02377117

C. Colas and B. I. Iorga, Virtual screening of the SAMPL4 blinded HIV integrase inhibitors dataset, J Comput Aided Mol Des, vol.28, issue.4, pp.455-462, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02377128

V. Y. Martiny, F. Martz, E. Selwa, and B. I. Iorga, Blind pose prediction, scoring, and affinity ranking of the CSAR 2014 dataset, Journal of chemical information and modeling, vol.56, issue.6, pp.996-1003, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02135548

E. Selwa, V. Y. Martiny, and B. I. Iorga, Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets, J Comput Aided Mol Des, vol.30, issue.9, pp.829-839, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02377131