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Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo\u2013apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins.<\/jats:p>","DOI":"10.3390\/informatics9010008","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:40:18Z","timestamp":1643143218000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4219-374X","authenticated-orcid":false,"given":"Zhonghua","family":"Xia","sequence":"first","affiliation":[{"name":"Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum f\u00fcr Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pavel","family":"Karpov","sequence":"additional","affiliation":[{"name":"Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum f\u00fcr Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2818-7498","authenticated-orcid":false,"given":"Grzegorz","family":"Popowicz","sequence":"additional","affiliation":[{"name":"Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum f\u00fcr Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Sattler","sequence":"additional","affiliation":[{"name":"Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum f\u00fcr Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany"},{"name":"Department of Chemistry, Bayerisches NMR-Zentrum, Technical University of Munich, D-85747 Garching, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6855-0012","authenticated-orcid":false,"given":"Igor V.","family":"Tetko","sequence":"additional","affiliation":[{"name":"Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum f\u00fcr Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany"},{"name":"BIGCHEM GmbH, D-85716 Unterschlei\u00dfheim, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/nrd2275","article-title":"Determining Druggability","volume":"6","author":"Owens","year":"2007","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1038\/nrd892","article-title":"The Druggable Genome","volume":"1","author":"Hopkins","year":"2002","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/nrd.2016.230","article-title":"A Comprehensive Map of Molecular Drug Targets","volume":"16","author":"Santos","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/nrd.2018.14","article-title":"Unexplored Therapeutic Opportunities in the Human Genome","volume":"17","author":"Oprea","year":"2018","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_5","first-page":"4236","article-title":"Crystal Structures of the Kinase Domain of C-Abl in Complex with the Small Molecule Inhibitors PD173955 and Imatinib (STI-571)","volume":"62","author":"Nagar","year":"2002","journal-title":"Cancer Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1126\/science.289.5486.1938","article-title":"Structural Mechanism for STI-571 Inhibition of Abelson Tyrosine Kinase","volume":"289","author":"Schindler","year":"2000","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.chembiol.2010.09.010","article-title":"Activation State-Dependent Binding of Small Molecule Kinase Inhibitors: Structural Insights from Biochemistry","volume":"17","author":"Wodicka","year":"2010","journal-title":"Chem. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Umezawa, K., and Kii, I. (2021). Druggable Transient Pockets in Protein Kinases. Molecules, 26.","DOI":"10.3390\/molecules26030651"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"eabd0480","DOI":"10.1126\/sciadv.abd0480","article-title":"Targeting the Cryptic Sites: NMR-Based Strategy to Improve Protein Druggability by Controlling the Conformational Equilibrium","volume":"6","author":"Mizukoshi","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11391","DOI":"10.1038\/ncomms11391","article-title":"Selective Inhibition of the Kinase DYRK1A by Targeting Its Folding Process","volume":"7","author":"Kii","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nprot.2015.043","article-title":"The FTMap Family of Web Servers for Determining and Characterizing Ligand-Binding Hot Spots of Proteins","volume":"10","author":"Kozakov","year":"2015","journal-title":"Nat. Protoc."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Clark, J.J., Benson, M.L., Smith, R.D., and Carlson, H.A. (2019). Inherent versus Induced Protein Flexibility: Comparisons within and between Apo and Holo Structures. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1006705"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1021\/acs.jcim.1c00204","article-title":"Finding Druggable Sites in Proteins Using TACTICS","volume":"61","author":"Evans","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1021\/jm030580l","article-title":"The PDBbind Database: Collection of Binding Affinities for Protein\u2212Ligand Complexes with Known Three-Dimensional Structures","volume":"47","author":"Wang","year":"2004","journal-title":"J. Med. Chem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.1021\/jm048957q","article-title":"The PDBbind Database: Methodologies and Updates","volume":"48","author":"Wang","year":"2005","journal-title":"J. Med. Chem."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1093\/bioinformatics\/btu789","article-title":"The Chemical Component Dictionary: Complete Descriptions of Constituent Molecules in Experimentally Determined 3D Macromolecules in the Protein Data Bank","volume":"31","author":"Westbrook","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res."},{"key":"ref_23","unstructured":"(2021). The PyMOL Molecular Graphics System, Schr\u00f6dinger, LLC. Version 2.4.0."},{"key":"ref_24","unstructured":"(2020). Schr\u00f6dinger Release 2020-3, Schr\u00f6dinger, LLC."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1111\/j.1747-0285.2007.00483.x","article-title":"New Method for Fast and Accurate Binding-Site Identification and Analysis","volume":"69","author":"Halgren","year":"2007","journal-title":"Chem. Biol. Drug Des."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1021\/ci800324m","article-title":"Identifying and Characterizing Binding Sites and Assessing Druggability","volume":"49","author":"Halgren","year":"2009","journal-title":"J. Chem. Inf. Model."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2894","DOI":"10.1021\/acs.jmedchem.8b01105","article-title":"Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile","volume":"62","author":"Lagarde","year":"2019","journal-title":"J. Med. Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10822-013-9644-8","article-title":"Protein and Ligand Preparation: Parameters, Protocols, and Influence on Virtual Screening Enrichments","volume":"27","author":"Adzhigirey","year":"2013","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s10822-007-9133-z","article-title":"Epik: A Software Program for PKaprediction and Protonation State Generation for Drug-like Molecules","volume":"21","author":"Shelley","year":"2007","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s10822-011-9440-2","article-title":"Online Chemical Modeling Environment (OCHEM): Web Platform for Data Storage, Model Development and Publishing of Chemical Information","volume":"25","author":"Sushko","year":"2011","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learning"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Perner, P. (2012). How Many Trees in a Random Forest?. Proceedings of the Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-31537-4"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1021\/ci010368v","article-title":"Prediction of N-Octanol\/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices","volume":"41","author":"Tetko","year":"2001","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1021\/ci000392t","article-title":"Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices","volume":"41","author":"Tetko","year":"2001","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1021\/ci025515j","article-title":"Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program","volume":"42","author":"Tetko","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1023\/A:1015952613760","article-title":"An Electrotopological-State Index for Atoms in Molecules","volume":"7","author":"Kier","year":"1990","journal-title":"Pharm. Res."},{"key":"ref_38","unstructured":"Kier, L.B., and Hall, L.H. (1999). Molecular Structure Description: The Electrotopological State, Elsevier Science."},{"key":"ref_39","unstructured":"(2021, November 20). Methods and Principles in Medicinal Chemistry Previous Volumes of This Series: Pharmacokinetics and Metabolism in Drug Design, Pharmacophores and Pharmacophore Searches Chirality in Drug Research Fragment-Based Approaches in Drug Discovery High-Throughput Screening in Drug Discovery Mass Spectrometry in Medicinal Chemistry Molecular Drug Properties Nuclear Receptors as Drug Targets. Available online: https:\/\/www.wiley.com\/en-us\/content-search?cq=Wiley%27s+Methods+and+Principles+in+Medicinal+Chemistry+Series&pq=Wiley%27s+Methods+and+Principles+in+Medicinal+Chemistry+Series."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1021\/ci00028a014","article-title":"Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information","volume":"35","author":"Hall","year":"1995","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_41","unstructured":"Shapley, L.S. (2016). A Value Fo N-Person Games, Princeton University Press."},{"key":"ref_42","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). A Unified Approach to Interpreting Model Predictions. Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS 2017), Curran Associates, Inc."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_44","unstructured":"Shrikumar, A., Greenside, P., and Kundaje, A. (2019). Learning Important Features Through Propagating Activation Differences. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.-R., and Samek, W. (2015). On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1002\/asmb.446","article-title":"Analysis of Regression in Game Theory Approach","volume":"17","author":"Lipovetsky","year":"2001","journal-title":"Appl. Stoch. Models Bus. Ind."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining Prediction Models and Individual Predictions with Feature Contributions","volume":"41","author":"Kononenko","year":"2014","journal-title":"Knowl. Inf. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Datta, A., Sen, S., and Zick, Y. (2016, January 22\u201326). Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems. Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2016.42"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-Connectivity Fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5349","DOI":"10.1002\/j.1460-2075.1996.tb00919.x","article-title":"MDMX: A Novel P53-Binding Protein with Some Functional Properties of MDM2","volume":"15","author":"Shvarts","year":"1996","journal-title":"EMBO J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.4161\/cc.9.6.10956","article-title":"Structures of Low Molecular Weight Inhibitors Bound to MDMX and MDM2 Reveal New Approaches for P53-MDMX\/MDM2 Antagonist Drug Discovery","volume":"9","author":"Popowicz","year":"2010","journal-title":"Cell Cycle"},{"key":"ref_54","unstructured":"(2020). AMBER 2020, University of California. Available online: https:\/\/ambermd.org\/doc12\/Amber20.pdf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.4161\/cc.6365","article-title":"Structure of the Human Mdmx Protein Bound to the P53 Tumor Suppressor Transactivation Domain","volume":"7","author":"Popowicz","year":"2008","journal-title":"Cell Cycle"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1021\/ja00344a001","article-title":"Quantum and Statistical Mechanical Studies of Liquids. 25. Solvation and Conformation of Methanol in Water","volume":"105","author":"Jorgensen","year":"2002","journal-title":"J. Am. Chem. Soc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1002\/(SICI)1096-987X(20000130)21:2<132::AID-JCC5>3.0.CO;2-P","article-title":"Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: I. Method","volume":"21","author":"Jakalian","year":"2000","journal-title":"J. Comput. Chem."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1002\/jcc.10128","article-title":"Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation","volume":"23","author":"Jakalian","year":"2002","journal-title":"J. Comput. Chem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3902","DOI":"10.1021\/ja00299a024","article-title":"Development and Use of Quantum Mechanical Molecular Models. 76. AM1: A New General Purpose Quantum Mechanical Molecular Model","volume":"107","author":"Dewar","year":"2002","journal-title":"J. Am. Chem. Soc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1002\/jcc.20035","article-title":"Development and Testing of a General Amber Force Field","volume":"25","author":"Wang","year":"2004","journal-title":"J. Comput. Chem."},{"key":"ref_61","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. Theory Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.4161\/cc.6.19.4740","article-title":"Molecular Basis for the Inhibition of P53 by Mdmx","volume":"6","author":"Popowicz","year":"2007","journal-title":"Cell Cycle"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/minf.201300030","article-title":"Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)","volume":"33","author":"Vorberg","year":"2014","journal-title":"Mol. Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13321-016-0113-y","article-title":"The Development of Models to Predict Melting and Pyrolysis Point Data Associated with Several Hundred Thousand Compounds Mined from PATENTS","volume":"8","author":"Tetko","year":"2016","journal-title":"J. Cheminform."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1021\/ci400213d","article-title":"Development of Dimethyl Sulfoxide Solubility Models Using 163\u2009000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions","volume":"53","author":"Tetko","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Salmina, E.S., Haider, N., and Tetko, I.V. (2016). Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds. Molecules, 21.","DOI":"10.3390\/molecules21010001"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"6810","DOI":"10.1021\/ja301056a","article-title":"Halogen-Enriched Fragment Libraries as Leads for Drug Rescue of Mutant P53","volume":"134","author":"Wilcken","year":"2012","journal-title":"J. Am. Chem. Soc."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.1021\/acs.jmedchem.6b00228","article-title":"Monoacidic Inhibitors of the Kelch-like ECH-Associated Protein 1: Nuclear Factor Erythroid 2-Related Factor 2 (KEAP1:NRF2) Protein\u2013Protein Interaction with High Cell Potency Identified by Fragment-Based Discovery","volume":"59","author":"Davies","year":"2016","journal-title":"J. Med. Chem."},{"key":"ref_69","unstructured":"Xia, Z. (2022). In Silico Structure-Based Approaches to Design Mdmx Inhibitors, Munich. [Ph.D. Thesis, Technische Universit\u00e4t M\u00fcnchen]."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"100573","DOI":"10.1016\/j.xcrp.2021.100573","article-title":"Combating Small-Molecule Aggregation with Machine Learning","volume":"2","author":"Lee","year":"2021","journal-title":"Cell Rep. Phys. Sci."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:07:00Z","timestamp":1760134020000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["informatics9010008"],"URL":"https:\/\/doi.org\/10.3390\/informatics9010008","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,25]]}}}