{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:01:14Z","timestamp":1769752874051,"version":"3.49.0"},"reference-count":68,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FCT\/MCTES","doi-asserted-by":"publisher","award":["UIDB\/50006\/2020"],"award-info":[{"award-number":["UIDB\/50006\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor\u2013ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.<\/jats:p>","DOI":"10.3390\/molecules28176379","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:45:51Z","timestamp":1693482351000},"page":"6379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2321-9467","authenticated-orcid":false,"given":"Shuvam","family":"Sar","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0664-0389","authenticated-orcid":false,"given":"Soumya","family":"Mitra","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India"},{"name":"Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0071-708X","authenticated-orcid":false,"given":"Parthasarathi","family":"Panda","sequence":"additional","affiliation":[{"name":"Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9079-489X","authenticated-orcid":false,"given":"Subhash C.","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0520-0952","authenticated-orcid":false,"given":"Nilanjan","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4818-9047","authenticated-orcid":false,"given":"Amit Kumar","family":"Halder","sequence":"additional","affiliation":[{"name":"Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, India"},{"name":"LAQV@REQUIMTE\u2014Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"Maria Natalia D. S.","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\u2014Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1080\/13543776.2022.2054329","article-title":"Soluble epoxide hydrolase inhibitors: An overview and patent review from the last decade","volume":"32","author":"Iyer","year":"2022","journal-title":"Expert Opin. Ther. Pat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0163-7827(03)00049-3","article-title":"Weintraub NL. Epoxyeicosatrienoic acids (EETs): Metabolism and biochemical function","volume":"43","author":"Spector","year":"2004","journal-title":"Prog. Lipid Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"99","DOI":"10.2174\/1389557522666220531152812","article-title":"A Comprehensive Review of Soluble Epoxide Hy\u00e5drolase Inhibitors Evaluating their Structure-Activity Relationship","volume":"23","author":"Nazari","year":"2023","journal-title":"Mini Rev. Med. Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9525","DOI":"10.1021\/acs.jmedchem.1c00831","article-title":"Second-Generation Dual FXR\/sEH Modulators with Optimized Pharmacokinetics","volume":"64","author":"Kaiser","year":"2021","journal-title":"J. Med. Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1517\/13543776.2010.484804","article-title":"Soluble epoxide hydrolase inhibitors: A patent review","volume":"20","author":"Shen","year":"2010","journal-title":"Expert Opin. Ther. Pat."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1124\/pr.113.007781","article-title":"The pharmacology of the cytochrome P450 epoxygenase\/soluble epoxide hydrolase axis in the vasculature and cardiovascular disease","volume":"66","author":"Fleming","year":"2014","journal-title":"Pharmacol. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16747","DOI":"10.1073\/pnas.0508081102","article-title":"The antiinflammatory effect of laminar flow: The role of PPAR\u03b3, epoxyeicosatrienoic acids, and soluble epoxide hydrolase","volume":"46","author":"Liu","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1016\/j.ijcard.2012.03.167","article-title":"A potent soluble epoxide hydrolase inhibitor, t-AUCB, acts through PPAR\u03b3 to modulate the function of endothelial progenitor cells from patients with acute myocardial infarction","volume":"4","author":"Xu","year":"2013","journal-title":"Int. J. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.pharmthera.2017.06.006","article-title":"Soluble epoxide hydrolase as a therapeutic target for pain, inflammatory and neurodegenerative diseases","volume":"180","author":"Wagner","year":"2017","journal-title":"Pharmacol. Ther."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1021\/acs.jmedchem.5b01239","article-title":"N-Benzylbenzamides: A Novel Merged Scaffold for Orally Available Dual Soluble Epoxide Hydrolase\/Peroxisome Proliferator-Activated Receptor \u03b3 Modulators","volume":"59","author":"Lamers","year":"2016","journal-title":"J. Med. Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1016\/j.bbrc.2012.02.108","article-title":"Role of soluble epoxide hydrolase phosphatase activity in the metabolism of lysophosphatidic acids","volume":"419","author":"Morisseau","year":"2012","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1194\/jlr.M022319","article-title":"Lysophosphatidic acids are new substrates for the phosphatase domain of soluble epoxide hydrolase","volume":"53","author":"Oguro","year":"2012","journal-title":"J. Lipid Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13524","DOI":"10.1038\/srep13524","article-title":"Role of phosphatase activity of soluble epoxide hydrolase in regulating simvastatin-activated endothelial nitric oxide synthase","volume":"5","author":"Hou","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8443","DOI":"10.1021\/acs.jmedchem.9b00445","article-title":"Discovery of the First in Vivo Active Inhibitors of the Soluble Epoxide Hydrolase Phosphatase Domain","volume":"62","author":"Kramer","year":"2019","journal-title":"J. Med. Chem."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dang, H., Li, D., Pang, W., Hammock, B.D., and Zhu, Y. (2012). Inhibition of soluble epoxide hydrolase attenuates high-fat-diet-induced hepatic steatosis by reduced systemic inflammatory status in mice. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0039165"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7703","DOI":"10.1021\/acs.jmedchem.7b00398","article-title":"A Dual Modulator of Farnesoid X Receptor and Soluble Epoxide Hydrolase to Counter Nonalcoholic Steatohepatitis","volume":"60","author":"Schmidt","year":"2017","journal-title":"J. Med. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.3389\/fphar.2018.01275","article-title":"QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery","volume":"9","author":"Neves","year":"2018","journal-title":"Front. Pharmacol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1080\/13543776.2018.1475560","article-title":"QSAR modelling: A therapeutic patent review 2010-present","volume":"28","author":"Halder","year":"2018","journal-title":"Expert Opin. Ther. Pat."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Halder, A.K., and Cordeiro, M.N.D.S. (2019). Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20174191"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13321-021-00508-0","article-title":"QSAR-Co-X: An open source toolkit for multitarget QSAR modelling","volume":"13","author":"Halder","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1039\/D0CS00098A","article-title":"QSAR without borders","volume":"49","author":"Muratov","year":"2020","journal-title":"Chem. Soc. Rev."},{"key":"ref_22","unstructured":"Roy, K. (2017). Advances in QSAR Modeling, Applications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences, Springer."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1021\/ci010368v","article-title":"Prediction of n-octanol\/water partition coefficients from PHYSPROP database using ar-tificial neural networks and E-state indices","volume":"41","author":"Tetko","year":"2001","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/S1093-3263(01)00123-1","article-title":"Beware of q2!","volume":"20","author":"Golbraikh","year":"2002","journal-title":"J. Mol. Graph. Model."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1021\/acs.jcim.6b00088","article-title":"A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology","volume":"56","author":"Gramatica","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Todeschini, R., and Consonni, V. (2000). Handbook of Molecular Descriptors, Wiley-VCH Verlag GmbH.","DOI":"10.1002\/9783527613106"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s10822-015-9893-9","article-title":"Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign","volume":"30","author":"Sliwoski","year":"2016","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1002\/minf.201200141","article-title":"Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for \u2018Orphan\u2019 Molecules","volume":"32","author":"Reutlinger","year":"2013","journal-title":"Mol. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17259","DOI":"10.1021\/acs.jmedchem.1c01331","article-title":"Structure-Based Design of Dual Partial Peroxisome Proliferator-Activated Receptor \u03b3 Agonists\/Soluble Epoxide Hydrolase Inhibitors","volume":"64","author":"Lillich","year":"2021","journal-title":"J. Med. Chem."},{"key":"ref_30","unstructured":"ACD\/ChemSketch, Advanced Chemistry Development, Inc. (ACD\/Labs). Available online: www.acdlabs.com."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/1758-2946-3-33","article-title":"Open Babel: An open chemical toolbox","volume":"3","author":"Banck","year":"2011","journal-title":"J. Cheminform."},{"key":"ref_32","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_33","first-page":"1000","article-title":"Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures","volume":"4","author":"Sadowski","year":"2002","journal-title":"J. Chem. Inf. Model."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of MDL keys for use in drug discovery","volume":"42","author":"Durant","year":"2002","journal-title":"J. Chem. Inf. Comp. Sci."},{"key":"ref_35","first-page":"2579","article-title":"Visualizing High-Dimensional Data Using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","unstructured":"Mauri, A. (2020). Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana."},{"key":"ref_37","unstructured":"De Sousa, J.M.A. (2017). Tutorials in Chemoinformatics, John Wiley & Sons, Ltd."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s10822-005-9008-0","article-title":"Substructural fragments: An universal language to encode reactions, molecular and supramolecular structures","volume":"19","author":"Varnek","year":"2005","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13321-018-0258-y","article-title":"Mordred: A molecular descriptor calculator","volume":"10","author":"Moriwaki","year":"2018","journal-title":"J. Cheminform."},{"key":"ref_40","first-page":"666","article-title":"Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms:\u2009 Definition and Comparison with the Other Descriptors","volume":"4","author":"Filimonov","year":"1999","journal-title":"J. Chem. Inf. Model."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10947-007-0023-y","article-title":"Modeling of drug molecule orientation within a receptor cavity in the BiS algorithm framework","volume":"48","author":"Potemkin","year":"2007","journal-title":"J. Struct. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.chemolab.2017.08.003","article-title":"PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors","volume":"169","author":"Masand","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.dental.2021.12.014","article-title":"First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers","volume":"38","author":"Halder","year":"2022","journal-title":"Dent. Mater."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2015.07.007","article-title":"\u201cNanoBRIDGES\u201d software: Open access tools to perform QSAR and nano-QSAR modeling","volume":"147","author":"Ambure","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1002\/qsar.200610151","article-title":"Principles of QSAR models validation: Internal and external","volume":"26","author":"Gramatica","year":"2007","journal-title":"QSAR Comb. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.3390\/molecules14051660","article-title":"On two novel parameters for validation of predictive QSAR models","volume":"14","author":"Roy","year":"2009","journal-title":"Molecules"},{"key":"ref_48","first-page":"9","article-title":"A Study of Effects of MultiCollinearity in the Multivariable Analysis","volume":"4","author":"Yoo","year":"2014","journal-title":"Int. J. Appl. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1002\/jcc.23361","article-title":"QSARINS: A new software for the development, analysis, and validation of QSAR MLR models","volume":"34","author":"Gramatica","year":"2013","journal-title":"J. Comput. Chem."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0169-7439(98)00124-5","article-title":"The K correlation index: Theory development and its application in chemometrics","volume":"46","author":"Todeschini","year":"1999","journal-title":"Chem. Intell. Lab. Sys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.chemolab.2011.08.007","article-title":"Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection","volume":"109","author":"Ojha","year":"2011","journal-title":"Chem. Intell. Lab. Sys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1004255","DOI":"10.3389\/fphar.2022.1004255","article-title":"In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus","volume":"13","author":"Ghosh","year":"2022","journal-title":"Front. Pharmacol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1093\/bioinformatics\/btz521","article-title":"MaNGA: A novel multi-niche multi-objective genetic algorithm for QSAR modelling","volume":"36","author":"Serra","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"160590","DOI":"10.1016\/j.scitotenv.2022.160590","article-title":"Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?","volume":"861","author":"Wyrzykowska","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/72.655045","article-title":"Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions","volume":"9","author":"Huang","year":"1998","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s11030-014-9565-z","article-title":"IMMAN: Free software for information theory-based chemometric analysis","volume":"19","author":"Urias","year":"2015","journal-title":"Mol. Divers."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"107625","DOI":"10.1016\/j.envint.2022.107625","article-title":"Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors","volume":"170","author":"Khan","year":"2022","journal-title":"Environ. Int."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Halder, A.K., Haghbakhsh, R., Voroshylova, I.V., Duarte, A.R.C., and Cordeiro, M.N.D.S. (2021). Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures. Molecules, 26.","DOI":"10.3390\/molecules26195779"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13321-020-00423-w","article-title":"Transformer-CNN: Swiss knife for QSAR modeling and interpretation","volume":"12","author":"Karpov","year":"2020","journal-title":"J. Cheminform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s00894-010-0684-x","article-title":"Open3DQSAR: A new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields","volume":"17","author":"Tosco","year":"2011","journal-title":"J. Mol. Model."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s10822-011-9462-9","article-title":"Open3DALIGN: An open-source software aimed at unsupervised ligand alignment","volume":"25","author":"Tosco","year":"2011","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1016\/j.bmcl.2015.02.076","article-title":"Three-dimensional rational approach to the discovery of potent substituted cyclopropyl urea soluble epoxide hydrolase inhibitors","volume":"25","author":"Takai","year":"2015","journal-title":"Bioorg Med. Chem. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Halder, A.K., and Cordeiro, M.N.D.S. (2021). Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules, 11.","DOI":"10.3390\/biom11111670"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1007\/s11224-019-01305-0","article-title":"Molecular alteration in drug susceptibility against subtype B and C-SA HIV-1 proteases: MD study","volume":"30","author":"Halder","year":"2019","journal-title":"Struct. Chem."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/07391102.1998.10508245","article-title":"Molecular dynamics and continuum solvent studies of the stability of polyG-polyC and polyA-polyT DNA duplexes in solution","volume":"16","author":"Cheatham","year":"1998","journal-title":"J. Biomol. Struct. Dyn."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/28\/17\/6379\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:44:11Z","timestamp":1760129051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/28\/17\/6379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":68,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["molecules28176379"],"URL":"https:\/\/doi.org\/10.3390\/molecules28176379","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,31]]}}}