{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T15:24:09Z","timestamp":1790868249847,"version":"4.1.0"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["23K11313"],"award-info":[{"award-number":["23K11313"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["22H03686"],"award-info":[{"award-number":["22H03686"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["25K03223"],"award-info":[{"award-number":["25K03223"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["23K28185"],"award-info":[{"award-number":["23K28185"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["23K24939"],"award-info":[{"award-number":["23K24939"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["21H03551"],"award-info":[{"award-number":["21H03551"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["25K03224"],"award-info":[{"award-number":["25K03224"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["25ama121029j0004"],"award-info":[{"award-number":["25ama121029j0004"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["25ama121026j0004"],"award-info":[{"award-number":["25ama121026j0004"]}]},{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","award":["25am0521016h0202"],"award-info":[{"award-number":["25am0521016h0202"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["23K11313"],"award-info":[{"award-number":["23K11313"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["22H03686"],"award-info":[{"award-number":["22H03686"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["25K03223"],"award-info":[{"award-number":["25K03223"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["23K28185"],"award-info":[{"award-number":["23K28185"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["23K24939"],"award-info":[{"award-number":["23K24939"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["21H03551"],"award-info":[{"award-number":["21H03551"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["25K03224"],"award-info":[{"award-number":["25K03224"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["25ama121029j0004"],"award-info":[{"award-number":["25ama121029j0004"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["25ama121026j0004"],"award-info":[{"award-number":["25ama121026j0004"]}]},{"name":"Basis for Supporting Innovative Drug Discovery and Life Science Research; BINDS","award":["25am0521016h0202"],"award-info":[{"award-number":["25am0521016h0202"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["23K11313"],"award-info":[{"award-number":["23K11313"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["22H03686"],"award-info":[{"award-number":["22H03686"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["25K03223"],"award-info":[{"award-number":["25K03223"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["23K28185"],"award-info":[{"award-number":["23K28185"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["23K24939"],"award-info":[{"award-number":["23K24939"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["21H03551"],"award-info":[{"award-number":["21H03551"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["25K03224"],"award-info":[{"award-number":["25K03224"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["25ama121029j0004"],"award-info":[{"award-number":["25ama121029j0004"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["25ama121026j0004"],"award-info":[{"award-number":["25ama121026j0004"]}]},{"name":"AMED Smart Bio Drug Discovery and Research Support Project","award":["25am0521016h0202"],"award-info":[{"award-number":["25am0521016h0202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challenging because they are often discovered serendipitously when both ligand-binding (holo) and ligand-free (apo) states are experimentally determined. Here, we introduce CrypTothML, a novel framework that integrates mixed-solvent molecular dynamics (MSMD) simulations and machine learning to predict cryptic sites accurately. CrypTothML first identifies hotspots through MSMD simulations using six chemically diverse probes (benzene, dimethyl-ether, phenol, methyl-imidazole, acetonitrile, and ethylene glycol). A machine learning model then ranks these hotspots based on their likelihood of being cryptic sites, incorporating both hotspot-derived and protein-specific features. Evaluation on a curated dataset demonstrated that CrypTothML outperforms recent machine learning-based methods, achieving an AUC-ROC of 0.88 and successfully identifying cryptic sites missed by other methods. Additionally, CrypTothML ranked cryptic sites as the top prediction more frequently than existing methods. This approach provides a powerful strategy for accelerating drug discovery and designing allosteric drugs.<\/jats:p>","DOI":"10.3390\/ijms26104710","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T12:22:17Z","timestamp":1747225337000},"page":"4710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction"],"prefix":"10.3390","volume":"26","author":[{"given":"Chie","family":"Motono","sequence":"first","affiliation":[{"name":"Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan"},{"name":"Integrated Research Center for Self-Care Technology (IRC-SCT), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0224-0035","authenticated-orcid":false,"given":"Keisuke","family":"Yanagisawa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo 152-8550, Japan"},{"name":"Middle Molecule IT-Based Drug Discovery Laboratory (MIDL), Institute of Science Tokyo, Tokyo 152-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Koseki","sequence":"additional","affiliation":[{"name":"Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5664-0566","authenticated-orcid":false,"given":"Kenichiro","family":"Imai","sequence":"additional","affiliation":[{"name":"Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan"},{"name":"Integrated Research Center for Self-Care Technology (IRC-SCT), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan"},{"name":"Global Research and Development Center for Business by Quantum-AI Technology (G-QuAT), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8560, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.tibs.2018.11.007","article-title":"Advanced methods for accessing protein shape-shifting present new therapeutic opportunities","volume":"44","author":"Knoverek","year":"2019","journal-title":"Trends Biochem. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cbpa.2018.05.003","article-title":"Cryptic binding sites on proteins: Definition, detection, and druggability","volume":"44","author":"Vajda","year":"2018","journal-title":"Curr. Opin. Chem. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.sbi.2016.06.017","article-title":"Computational approaches to investigating allostery","volume":"41","author":"Wodak","year":"2016","journal-title":"Curr. Opin. Struct. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hart, K.M., Moeder, K.E., Ho, C.M.W., Zimmerman, M.I., Frederick, T.E., and Bowman, G.R. (2017). Designing small molecules to target cryptic pockets yields both positive and negative allosteric modulators. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0178678"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.sbi.2004.10.009","article-title":"Searching for new allosteric sites in enzymes","volume":"14","author":"Hardy","year":"2004","journal-title":"Curr. Opin. Struct. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1021\/acs.accounts.9b00613","article-title":"Investigating cryptic binding sites by Molecular Dynamics simulations","volume":"53","author":"Kuzmanic","year":"2020","journal-title":"Acc. Chem. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1021\/acs.jcim.6b00623","article-title":"Deciphering cryptic binding sites on proteins by mixed-solvent Molecular Dynamics","volume":"57","author":"Kimura","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1021\/acs.jctc.8b01295","article-title":"Cosolvent-enhanced sampling and unbiased identification of cryptic pockets suitable for structure-based drug design","volume":"15","author":"Schmidt","year":"2019","journal-title":"J. Chem. Theory Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1021\/acs.jcim.9b01209","article-title":"PlayMolecule CrypticScout: Predicting protein cryptic sites using mixed-solvent Molecular Simulations","volume":"60","author":"Lovera","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"14257","DOI":"10.1021\/jacs.6b05425","article-title":"Understanding cryptic pocket formation in protein targets by enhanced sampling simulations","volume":"138","author":"Oleinikovas","year":"2016","journal-title":"J. Am. Chem. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1021\/acs.jcim.0c01002","article-title":"Identification of cryptic binding sites using MixMD with standard and accelerated Molecular Dynamics","volume":"61","author":"Smith","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7743","DOI":"10.1021\/acs.jcim.4c01189","article-title":"Combined physics- and machine-learning-based method to identify druggable binding sites using SILCS-hotspots","volume":"64","author":"Nordquist","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"MacKerell, A.D., Jo, S., Lakkaraju, S.K., Lind, C., and Yu, W. (2020). Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots). Biochim. Biophys. Acta Gen. Subj., 1864.","DOI":"10.1016\/j.bbagen.2020.129519"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guvench, O., and MacKerell, A.D. (2009). Computational fragment-based binding site identification by ligand competitive saturation. PLoS Comput. Biol., 5.","DOI":"10.1371\/journal.pcbi.1000435"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1021\/ci4005628","article-title":"Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach","volume":"53","author":"Raman","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1021\/acs.jctc.8b00263","article-title":"Exploring cryptic pockets formation in targets of pharmaceutical interest with SWISH","volume":"14","author":"Comitani","year":"2018","journal-title":"J. Chem. Theory Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1021\/acs.jctc.3c01318","article-title":"SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein\u2013Protein Interfaces","volume":"20","author":"Borsatto","year":"2024","journal-title":"J. Chem. Theory Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1021\/jm801385d","article-title":"Binding site detection and druggability index from first principles","volume":"52","author":"Seco","year":"2009","journal-title":"J. Med. Chem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8530","DOI":"10.1021\/jm5010418","article-title":"Molecular simulations with solvent competition quantify water displaceability and provide accurate interaction maps of protein binding sites","volume":"57","author":"Barril","year":"2014","journal-title":"J. Med. Chem."},{"key":"ref_20","first-page":"62","article-title":"Extracting atomic contributions to binding free energy using Molecular Dynamics simulations with mixed solvents (MDmix)","volume":"19","author":"Schmidtke","year":"2022","journal-title":"Curr. Drug Discov. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.jmb.2016.01.029","article-title":"CryptoSite: Expanding the druggable proteome by characterization and prediction of cryptic binding sites","volume":"428","author":"Cimermancic","year":"2016","journal-title":"J. Mol. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"W271","DOI":"10.1093\/nar\/gks441","article-title":"FTMAP: Extended protein mapping with user-selected probe molecules","volume":"40","author":"Ngan","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Capra, J.A., Laskowski, R.A., Thornton, J.M., Singh, M., and Funkhouser, T.A. (2009). Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput. Biol., 5.","DOI":"10.1371\/journal.pcbi.1000585"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"E3416","DOI":"10.1073\/pnas.1711490115","article-title":"Exploring the structural origins of cryptic sites on proteins","volume":"115","author":"Beglov","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Koseki, J., Motono, C., Yanagisawa, K., Kudo, G., Yoshino, R., Hirokawa, T., and Imai, K. (2025). CrypToth: Cryptic pocket detection through mixed-solvent molecular dynamics simulations based topological data analysis. J. Chem. Inf. Model., in press.","DOI":"10.1101\/2024.07.10.602991"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16074","DOI":"10.1073\/pnas.0708036104","article-title":"A surface on the androgen receptor that allosterically regulates coactivator binding","volume":"104","author":"Arnold","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1110\/ps.062247506","article-title":"Localization of Binding Sites in Protein Structures by Optimization of a Composite Scoring Function","volume":"15","author":"Rossi","year":"2006","journal-title":"Protein Sci."},{"key":"ref_28","first-page":"369","article-title":"Hydrophobicity Parameters \u03c0 of amino acid side chains from partitioning of N-Acetyl-amino-acid amides","volume":"18","author":"Fauchere","year":"1983","journal-title":"Eur. J. Med. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/nmeth.4067","article-title":"CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins","volume":"14","author":"Huang","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1038\/s41467-023-36699-3","article-title":"Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network","volume":"14","author":"Meller","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/j.bmcl.2019.01.035","article-title":"Structure-based design, synthesis and biological evaluation of a novel series of isoquinolone and pyrazolo [4,3-c]pyridine inhibitors of fascin 1 as potential anti-metastatic agents","volume":"29","author":"Francis","year":"2019","journal-title":"Bioorg. Med. Chem. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8685","DOI":"10.1021\/acs.jpcb.6b03515","article-title":"Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems","volume":"120","author":"Ghanakota","year":"2016","journal-title":"J. Phys. Chem. B"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1021\/acs.jcim.8b00265","article-title":"MixMD Probeview: Robust Binding Site Prediction from Cosolvent Simulations","volume":"58","author":"Graham","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1016\/j.str.2015.09.010","article-title":"Structure of the Complex of Human Programmed Death 1, PD-1, and Its Ligand PD-L1","volume":"23","author":"Zak","year":"2015","journal-title":"Structure"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11614","DOI":"10.1021\/acs.jmedchem.1c00957","article-title":"Terphenyl-Based Small-Molecule Inhibitors of Programmed Cell Death-1\/Programmed Death-Ligand 1 Protein\u2013Protein Interaction","volume":"64","author":"Muszak","year":"2021","journal-title":"J. Med. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"\u0160krh\u00e1k, V., Novotn\u00fd, M., Feidakis, C.P., Kriv\u00e1k, R., and Hoksza, D. (2025). CryptoBench: Cryptic Protein\u2013Ligand Binding Sites Dataset and Benchmark. Bioinformatics, 41.","DOI":"10.1093\/bioinformatics\/btae745"},{"key":"ref_37","unstructured":"Schr\u00f6dinger, LLC (2020). Schr\u00f6dinger Release 2020-4, Schr\u00f6dinger, LLC."},{"key":"ref_38","unstructured":"Frisch, M.J., Trucks, G., Schlegel, H.B., Scuseria, G., Robb, M., Cheeseman, J., Scalmani, G., Barone, V., Petersson, G., and Nakatsuji, H. (2016). Gaussian16, Gaussian Incorporated."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1021\/acs.jcim.1c00134","article-title":"EXPRORER: Rational cosolvent set construction method for cosolvent Molecular Dynamics using large-scale computation","volume":"61","author":"Yanagisawa","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1002\/jcc.21224","article-title":"PACKMOL: A package for building initial configurations for molecular dynamics simulations","volume":"30","author":"Andrade","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3696","DOI":"10.1021\/acs.jctc.5b00255","article-title":"ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB","volume":"11","author":"Maier","year":"2015","journal-title":"J. Chem. Theor. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"114502","DOI":"10.1063\/5.0019056","article-title":"A fast and high-quality charge model for the next generation general AMBER force field","volume":"153","author":"He","year":"2020","journal-title":"J. Chem. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1063\/1.445869","article-title":"Comparison of simple potential functions for simulating liquid water","volume":"79","author":"Jorgensen","year":"1983","journal-title":"J. Chem. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.csbj.2024.12.017","article-title":"Predicting fragment binding modes using customized Lennard-Jones potentials in short molecular dynamics simulations","volume":"27","author":"Vorreiter","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1021\/ct700200b","article-title":"P-LINCS: A parallel linear constraint solver for molecular simulation","volume":"4","author":"Hess","year":"2008","journal-title":"J. Chem. Theor. Comput."},{"key":"ref_46","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD\u201996), Portland, OR, USA."},{"key":"ref_47","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-Generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1021\/acs.jcim.1c01164","article-title":"Divide and Conquer. Pocket-Opening Mixed-Solvent Simulations in the Perspective of Docking Virtual Screening Applications for Drug Discovery","volume":"62","author":"Zariquiey","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1021\/acs.jcim.0c01126","article-title":"Small Glycols Discover Cryptic Pockets on Proteins for Fragment-Based Approaches","volume":"61","author":"Bansia","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1021\/acs.jctc.1c01177","article-title":"Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites","volume":"18","author":"Tan","year":"2022","journal-title":"J. Chem. Theory Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6633","DOI":"10.1021\/acs.jctc.0c00405","article-title":"Straightforward Incorporation of Multiple Ligand Types into Molecular Dynamics Simulations for Efficient Binding Site Detection and Characterization","volume":"16","author":"Tan","year":"2020","journal-title":"J. Chem. Theory Comput."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/10\/4710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:32:49Z","timestamp":1760031169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/10\/4710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["ijms26104710"],"URL":"https:\/\/doi.org\/10.3390\/ijms26104710","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}