{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T09:13:54Z","timestamp":1774343634613,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["NIH intramural research programs of NCATS"],"award-info":[{"award-number":["NIH intramural research programs of NCATS"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>The rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for antivirals that are resilient to resistance. Current Food and Drug Administration (FDA)-approved therapies primarily target single viral mechanisms, leaving gaps in efficacy. Here, we developed a Deep Learning-based Activity Screening Model (DLASM), which integrates graph convolutional network with machine learning to identify SARS-CoV-2 inhibitors, using experimental 3-chymotrypsin-like (3CL) main protease assay data. The optimized DLASMs virtually screened ~170,000 compounds from diverse in-house collections and yielded novel hits, several of which not only inhibited the 3CL protease but also blocked viral entry by interfering with heparan sulfate-mediated host interactions. These activities were validated through multiple assays, including 3CL enzymatic inhibition, SARS-CoV-2 pseudotyped particle entry, \u03b1-synuclein fibril uptake as a proxy for endocytosis, live virus cytopathic effect, heparan sulfate-dependent entry assay, and a 3D human lung mucociliary tissue model. Molecular docking studies elucidated binding modes at the 3CL protease active site, while molecular dynamics simulations provided insights into compound\u2013heparan sulfate interactions. The identified compounds represent early-stage hits with moderate potency that demonstrate dual-mechanism antiviral activity. Together, these findings establish dual-target inhibition as a promising antiviral strategy, offering not only enhanced potency but also reduced risk of resistance. Moreover, our DLASM framework provides a generalizable pipeline for identifying chemically diverse scaffolds and for broader applications beyond SARS-CoV-2.<\/jats:p>","DOI":"10.3390\/molecules31061043","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:02:43Z","timestamp":1774026163000},"page":"1043","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Guided Discovery of Dual Inhibitors of SARS-CoV-2 Entry and 3CL Protease"],"prefix":"10.3390","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-9007","authenticated-orcid":false,"given":"Peng","family":"Gao","sequence":"first","affiliation":[{"name":"Therapeutics Development Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Pavlinov","sequence":"additional","affiliation":[{"name":"Therapeutics Development Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Xu","sequence":"additional","affiliation":[{"name":"Therapeutics Development Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6900-6553","authenticated-orcid":false,"given":"Catherine Z.","family":"Chen","sequence":"additional","affiliation":[{"name":"Therapeutics Development Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7357-9821","authenticated-orcid":false,"given":"Desarey","family":"Morales Vasquez","sequence":"additional","affiliation":[{"name":"Texas Biomedical Research Institute, San Antonio, TX 78227, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Therapeutics Development Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Ye","sequence":"additional","affiliation":[{"name":"National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, MD 20892, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7084-0804","authenticated-orcid":false,"given":"Luis","family":"Martinez-Sobrido","sequence":"additional","affiliation":[{"name":"Texas Biomedical Research Institute, San Antonio, TX 78227, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1034-0757","authenticated-orcid":false,"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Therapeutics Development Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8218-0433","authenticated-orcid":false,"given":"Min","family":"Shen","sequence":"additional","affiliation":[{"name":"Early Translation Branch, Division of Preclinical Innovation, National Center for Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.immuni.2020.03.007","article-title":"SARS-CoV-2 vaccines: Status report","volume":"52","author":"Amanat","year":"2020","journal-title":"Immunity"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.cell.2020.04.011","article-title":"The architecture of SARS-CoV-2 transcriptome","volume":"181","author":"Kim","year":"2020","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijid.2020.03.004","article-title":"The SARS-CoV-2 outbreak: What we know","volume":"94","author":"Wu","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/s41586-020-2798-3","article-title":"SARS-CoV-2 vaccines in development","volume":"586","author":"Krammer","year":"2020","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.cell.2020.09.033","article-title":"SARS-CoV-2 infection depends on cellular heparan sulfate and ACE2","volume":"183","author":"Clausen","year":"2020","journal-title":"Cell"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41421-020-00222-5","article-title":"Heparan sulfate assists SARS-CoV-2 in cell entry and can be targeted by approved drugs in vitro","volume":"6","author":"Zhang","year":"2020","journal-title":"Cell Discov."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1021\/acscentsci.0c00984","article-title":"Targeting the SARS-CoV-2 RNA genome with small molecule binders and ribonuclease targeting chimera (RIBOTAC) degraders","volume":"6","author":"Haniff","year":"2020","journal-title":"ACS Cent. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1021\/acs.jcim.2c00222","article-title":"Graph convolutional network-based screening strategy for rapid identification of SARS-CoV-2 cell-entry inhibitors","volume":"62","author":"Gao","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1126\/science.abl4784","article-title":"An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19","volume":"374","author":"Owen","year":"2021","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1038\/s41586-020-2223-y","article-title":"Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors","volume":"582","author":"Jin","year":"2020","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1002\/cpt.2646","article-title":"Recommendations for the management of drug\u2013drug interactions between the COVID-19 antiviral nirmatrelvir\/ritonavir (Paxlovid) and comedications","volume":"112","author":"Marzolini","year":"2022","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1056\/NEJMoa2204919","article-title":"Nirmatrelvir use and severe Covid-19 outcomes during the Omicron surge","volume":"387","author":"Arbel","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1021\/acsptsci.0c00108","article-title":"Identification of SARS-CoV-2 3CL protease inhibitors by a quantitative high-throughput screening","volume":"3","author":"Zhu","year":"2020","journal-title":"ACS Pharmacol. Transl. Sci."},{"key":"ref_14","first-page":"9","article-title":"Cytotoxic profiling of annotated and diverse chemical libraries using quantitative high-throughput screening","volume":"25","author":"Lee","year":"2020","journal-title":"SLAS Discov. Adv. Life Sci. RD"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1038\/s41467-020-16201-z","article-title":"Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost","volume":"11","author":"Guan","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1021\/acs.jcim.0c00195","article-title":"Neural Message Passing for NMR Chemical Shift Prediction","volume":"60","author":"Kwon","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1039\/C9SC03854J","article-title":"IMPRESSION\u2013prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy","volume":"11","author":"Gerrard","year":"2020","journal-title":"Chem. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2008","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4678","DOI":"10.1021\/jacs.8b00947","article-title":"Chemical pressure-driven enhancement of the hydrogen evolving activity of Ni2P from nonmetal surface doping interpreted via machine learning","volume":"140","author":"Wexler","year":"2018","journal-title":"J. Am. Chem. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9844","DOI":"10.1021\/jacs.8b02717","article-title":"Machine learning directed search for ultraincompressible, superhard materials","volume":"140","author":"Oliynyk","year":"2018","journal-title":"J. Am. Chem. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3309","DOI":"10.1021\/acs.jpclett.5b01456","article-title":"Machine learning for quantum mechanical properties of atoms in molecules","volume":"6","author":"Rupp","year":"2015","journal-title":"J. Phys. Chem. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9063","DOI":"10.1021\/jacs.9b03591","article-title":"Accelerated discovery of organic polymer photocatalysts for hydrogen evolution from water through the integration of experiment and theory","volume":"141","author":"Bai","year":"2019","journal-title":"J. Am. Chem. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17508","DOI":"10.1021\/jacs.8b08800","article-title":"Machine-learning prediction of CO adsorption in thiolated, Ag-alloyed Au nanoclusters","volume":"140","author":"Panapitiya","year":"2018","journal-title":"J. Am. Chem. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1021\/acs.jcim.9b00266","article-title":"Deep learning in chemistry","volume":"59","author":"Mater","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5255","DOI":"10.1021\/acs.jctc.7b00577","article-title":"Prediction errors of molecular machine learning models lower than hybrid DFT error","volume":"13","author":"Faber","year":"2017","journal-title":"J. Chem. Theory Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"241734","DOI":"10.1063\/1.5023671","article-title":"Accelerating atomic structure search with cluster regularization","volume":"148","author":"Bruix","year":"2018","journal-title":"J. Chem. Phys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3746","DOI":"10.1021\/acs.jcim.0c00388","article-title":"General Protocol for the Accurate Prediction of Molecular 13C\/1H NMR Chemical Shifts via Machine Learning Augmented DFT","volume":"60","author":"Gao","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12828","DOI":"10.1002\/anie.201703114","article-title":"First principles neural network potentials for reactive simulations of large molecular and condensed systems","volume":"56","author":"Behler","year":"2017","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"170901","DOI":"10.1063\/1.4966192","article-title":"Perspective: Machine learning potentials for atomistic simulations","volume":"145","author":"Behler","year":"2016","journal-title":"J. Chem. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9812","DOI":"10.1021\/acs.jpclett.0c02654","article-title":"Toward Accurate Predictions of Atomic Properties via Quantum Mechanics Descriptors Augmented Graph Convolutional Neural Network: Application of This Novel Approach in NMR Chemical Shifts Predictions","volume":"11","author":"Gao","year":"2020","journal-title":"J. Phys. Chem. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1021\/acscentsci.8b00913","article-title":"Machine learning of coarse-grained molecular dynamics force fields","volume":"5","author":"Wang","year":"2019","journal-title":"ACS Cent. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1021\/acs.jpcc.6b10908","article-title":"Machine learning force fields: Construction, validation, and outlook","volume":"121","author":"Botu","year":"2017","journal-title":"J. Phys. Chem. C"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"14817","DOI":"10.1039\/C5NR03903G","article-title":"Global minimization of gold clusters by combining neural network potentials and the basin-hopping method","volume":"7","author":"Ouyang","year":"2015","journal-title":"Nanoscale"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134104","DOI":"10.1063\/1.5048290","article-title":"Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies","volume":"149","author":"Meldgaard","year":"2018","journal-title":"J. Chem. Phys."},{"key":"ref_35","unstructured":"Lu, C., Liu, Q., Wang, C., Huang, Z., Lin, P., and He, L. (2019\u20131, January 27). Molecular property prediction: A multilevel quantum interactions modeling perspective. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13242","DOI":"10.1039\/D1CP00677K","article-title":"A general QSPR protocol for the prediction of atomic\/inter-atomic properties: A fragment based graph convolutional neural network (F-GCN)","volume":"23","author":"Gao","year":"2021","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_37","unstructured":"Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., and Gai, Y. (2019). Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"241722","DOI":"10.1063\/1.5019779","article-title":"Schnet\u2013a deep learning architecture for molecules and materials","volume":"148","author":"Sauceda","year":"2018","journal-title":"J. Chem. Phys."},{"key":"ref_39","unstructured":"Chen, G., Chen, P., Hsieh, C.-Y., Lee, C.-K., Liao, B., Liao, R., Liu, W., Qiu, J., Sun, Q., and Tang, J. (2019). Alchemy: A quantum chemistry dataset for benchmarking ai models. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"15084","DOI":"10.1021\/acs.jmedchem.3c00490","article-title":"Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates","volume":"66","author":"Gao","year":"2023","journal-title":"J. Med. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, C.Z., Shinn, P., Itkin, Z., Eastman, R.T., Bostwick, R., Rasmussen, L., Huang, R., Shen, M., Hu, X., and Wilson, K.M. (2021). Drug repurposing screen for compounds inhibiting the cytopathic effect of SARS-CoV-2. Front. Pharmacol., 11.","DOI":"10.3389\/fphar.2020.592737"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1126\/science.1227157","article-title":"Pathological \u03b1-synuclein transmission initiates Parkinson-like neurodegeneration in nontransgenic mice","volume":"338","author":"Luk","year":"2012","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5777","DOI":"10.1038\/s41467-023-41453-w","article-title":"Host heparan sulfate promotes ACE2 super-cluster assembly and enhances SARS-CoV-2-associated syncytium formation","volume":"14","author":"Zhang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5249","DOI":"10.1021\/jacs.7b01923","article-title":"Synthesis of 3-O-sulfated oligosaccharides to understand the relationship between structures and functions of heparan sulfate","volume":"139","author":"Wang","year":"2017","journal-title":"J. Am. Chem. Soc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"113995","DOI":"10.1016\/j.jviromet.2020.113995","article-title":"Rapid in vitro assays for screening neutralizing antibodies and antivirals against SARS-CoV-2","volume":"287","author":"Park","year":"2021","journal-title":"J. Virol. Methods"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"McAuley, A.J., Jansen van Vuren, P., Mohammed, M.-U.-R., Goldie, S., Riddell, S., G\u00f6dde, N.J., Styles, I.K., Bruce, M.P., and Chahal, S. (2022). Use of Human Lung Tissue Models for Screening of Drugs against SARS-CoV-2 Infection. Viruses, 14.","DOI":"10.20944\/preprints202209.0288.v1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"144801","DOI":"10.1063\/5.0138367","article-title":"SchNetPack 2.0: A neural network toolbox for atomistic machine learning","volume":"158","author":"Hessmann","year":"2023","journal-title":"J. Chem. Phys."},{"key":"ref_48","first-page":"4","article-title":"Rdkit documentation","volume":"1","author":"Landrum","year":"2013","journal-title":"Release"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, T. (2016). XGBoost: A Scalable Tree Boosting System. KDD \u201816: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_51","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. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1021\/acsinfecdis.0c00349","article-title":"The SARS-CoV-2 cytopathic effect is blocked by lysosome alkalizing small molecules","volume":"7","author":"Gorshkov","year":"2020","journal-title":"ACS Infect. Dis."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.slasd.2021.12.005","article-title":"A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry","volume":"27","author":"Xu","year":"2022","journal-title":"Slas Discov."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6294","DOI":"10.1038\/s41598-022-10293-x","article-title":"Mitoxantrone modulates a heparan sulfate-spike complex to inhibit SARS-CoV-2 infection","volume":"12","author":"Zhang","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_56","unstructured":"Frisch, M., Caricato, M., Hiscocks, J., and Michael, J. (2009). Gaussian 09, Gaussian Inc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1002\/jcc.24660","article-title":"CHARMM-GUI 10 years for biomolecular modeling and simulation","volume":"38","author":"Jo","year":"2017","journal-title":"J. Comput. Chem."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1002\/jcc.20065","article-title":"Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations","volume":"25","author":"Mackerell","year":"2004","journal-title":"J. Comput. Chem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1021\/jp973084f","article-title":"All-atom empirical potential for molecular modeling and dynamics studies of proteins","volume":"102","author":"MacKerell","year":"1998","journal-title":"J. Phys. Chem. B"},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"014101","DOI":"10.1063\/1.2408420","article-title":"Canonical sampling through velocity rescaling","volume":"126","author":"Bussi","year":"2007","journal-title":"J. Chem. Phys."},{"key":"ref_62","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. Theory Comput."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1002\/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H","article-title":"LINCS: A linear constraint solver for molecular simulations","volume":"18","author":"Hess","year":"1997","journal-title":"J. Comput. Chem."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"10089","DOI":"10.1063\/1.464397","article-title":"Particle mesh Ewald: An N log (N) method for Ewald sums in large systems","volume":"98","author":"Darden","year":"1993","journal-title":"J. Chem. Phys."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1021\/ci500020m","article-title":"g_mmpbsa\ue5f8 A GROMACS tool for high-throughput MM-PBSA calculations","volume":"54","author":"Kumari","year":"2014","journal-title":"J. Chem. Inf. Model."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/6\/1043\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T05:41:28Z","timestamp":1774330888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/6\/1043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":65,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["molecules31061043"],"URL":"https:\/\/doi.org\/10.3390\/molecules31061043","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]}}}