{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:14:30Z","timestamp":1792790070481,"version":"4.5.0"},"reference-count":101,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH","award":["U19 AG074879"],"award-info":[{"award-number":["U19 AG074879"]}]},{"name":"NIH","award":["R01 AG071470"],"award-info":[{"award-number":["R01 AG071470"]}]},{"name":"NIH","award":["U01 AG066833"],"award-info":[{"award-number":["U01 AG066833"]}]},{"name":"NIH","award":["U01 AG068057"],"award-info":[{"award-number":["U01 AG068057"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Targets"],"abstract":"<jats:p>Alzheimer\u2019s disease is a neurodegenerative disease that continues to have a rising number of cases. While extensive research has been conducted on Alzheimer\u2019s disease in the last few decades, only a few drugs have been approved by the FDA for its treatment, and even fewer aim to be curative rather than manage symptoms. There remains an urgent need to understand disease pathogenesis, as well as identify new targets for further drug discovery. Alzheimer\u2019s disease (AD) is known to stem from the build-up of amyloid beta (A\u03b2) plaques, as well as tangles of tau proteins. Furthermore, inflammation in the brain is known to arise from the degeneration of tissue and the build-up of insoluble material. Therefore, there is a potential link between the pathology of AD and inflammation in the brain, especially as the disease progresses to later stages, where neuronal death and degeneration levels are higher. Proteins that are relevant to both brain inflammation and AD, thus, make ideal potential targets for therapeutics; however, the proteins need to be evaluated to determine which targets would be ideal for potential drug therapeutic treatments, or \u2018druggable\u2019 targets. Druggability analysis was conducted using two structure-based methods (i.e., drug-like density analysis and SiteMap), as well as a sequence-based approach, SPIDER. The most druggable targets were then evaluated using single-nucleus sequencing data for their clinical relevance to inflammation in AD. For each of the top five targets, small molecule docking was used to evaluate which FDA approved drugs were able to bind with the chosen proteins. The top targets included DRD2 (inhibits adenylyl cyclase activity), C9 (binds with C5B8 to form the membrane attack complex), C4b (binds with C2a to form C3 convertase), C5AR1 (a GPCR that binds C5a), and GABA-A-R (the GPCR involved in inhibiting neurotransmission). Each target had multiple potential inhibitors from the FDA-approved drug list with decent binding infinities. Among these inhibitors, two drugs were found to be top inhibitors for more than one protein target. They were C15H14N2O2 and v316 (paracetamol), originally used to treat pain\/inflammation for cataracts and relieve headaches\/fever, respectively. These results provide the groundwork for further experimental investigations or clinical trials.<\/jats:p>","DOI":"10.3390\/targets2040025","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T11:03:08Z","timestamp":1733310188000},"page":"446-469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Repurposing FDA-Approved Drugs Against Potential Drug Targets Involved in Brain Inflammation Contributing to Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6158-6924","authenticated-orcid":false,"given":"Catherine","family":"Sharo","sequence":"first","affiliation":[{"name":"Department of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9712-9132","authenticated-orcid":false,"given":"Tianhua","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxuan","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4620-6154","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Garcia-Epelboim","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Mamourian","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5443-0503","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuyi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.trci.2018.06.014","article-title":"Inflammation as a central mechanism in Alzheimer\u2019s disease","volume":"4","author":"Kinney","year":"2018","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.drudis.2022.01.016","article-title":"Challenges and hopes for Alzheimer\u2019s disease","volume":"27","author":"Tatulian","year":"2022","journal-title":"Drug Discov. Today"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.3233\/JAD-2010-100975","article-title":"Alzheimer\u2019s Disease: A General Introduction and Pathomechanism","volume":"22","author":"Finder","year":"2010","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1016\/j.febslet.2006.06.021","article-title":"Involvement of in the abnormal hyperphosphorylation of tau and its reversal by Memantine","volume":"580","author":"Chohan","year":"2006","journal-title":"FEBS Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112915","DOI":"10.1016\/j.ejmech.2020.112915","article-title":"Small molecule therapeutics for tauopathy in Alzheimer\u2019s disease: Walking on the path of most resistance","volume":"209","author":"Wang","year":"2021","journal-title":"Eur. J. Med. Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1136\/bmj.p73","article-title":"Alzheimer\u2019s disease: FDA approves lecanemab amid cost and safety concerns","volume":"380","author":"Mahase","year":"2023","journal-title":"BMJ"},{"key":"ref_7","first-page":"6","article-title":"FDA approves new treatment for Alzheimer\u2019s disease","volume":"33","author":"Canady","year":"2023","journal-title":"Ment. Health Wkly."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41392-021-00762-6","article-title":"Wnt\/\u03b2-catenin signalling: Function, biological mechanisms, and therapeutic opportunities","volume":"7","author":"Liu","year":"2022","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/S0197-4580(00)00124-X","article-title":"Inflammation and Alzheimer\u2019s disease","volume":"21","author":"Akiyama","year":"2000","journal-title":"Neurobiol. Aging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.bbi.2004.01.004","article-title":"The influence of systemic inflammation on inflammation in the brain: Implications for chronic neurodegenerative disease","volume":"18","author":"Perry","year":"2004","journal-title":"Brain Behav. Immun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"175628641879110","DOI":"10.1177\/1756286418791107","article-title":"Can inflammation be resolved in Alzheimer\u2019s disease?","volume":"11","author":"Zhu","year":"2018","journal-title":"Ther. Adv. Neurol. Disord."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1007\/s12640-019-00057-3","article-title":"Microbiota Alterations in Alzheimer\u2019s Disease: Involvement of the Kynurenine Pathway and Inflammation","volume":"36","author":"Garcez","year":"2019","journal-title":"Neurotox. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00281-013-0382-8","article-title":"Microglia and macrophages of the central nervous system: The contribution of microglia priming and systemic inflammation to chronic neurodegeneration","volume":"35","author":"Perry","year":"2013","journal-title":"Semin. Immunopathol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s12974-015-0339-z","article-title":"Pharmacological antagonism of interleukin-8 receptor CXCR2 inhibits inflammatory reactivity and is neuroprotective in an animal model of Alzheimer\u2019s disease","volume":"12","author":"Ryu","year":"2015","journal-title":"J. Neuroinflammation"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lundstrom, K. (2009). An Overview on GPCRs and Drug Discovery: Structure-Based Drug Design and Structural Biology on GPCRs. G Protein-Coupled Receptors in Drug Discovery, Humana Press.","DOI":"10.1007\/978-1-60327-317-6_4"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1021\/ci100312t","article-title":"Drug-like Density: A Method of Quantifying the \u201cBindability\u201d of a Protein Target Based on a Very Large Set of Pockets and Drug-like Ligands from the Protein Data Bank","volume":"50","author":"Sheridan","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hanes, R., Liu, Y., and Huang, Z. (2024). Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes. Microorganisms, 12.","DOI":"10.3390\/microorganisms12061073"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1074\/mcp.M400159-MCP200","article-title":"Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes","volume":"4","author":"An","year":"2005","journal-title":"Mol. Cell. Proteom."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1021\/ci5006004","article-title":"PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties","volume":"55","author":"Borrel","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_20","unstructured":"Rodriguez-Salarichs, J. (2024, May 05). PockDrug Descriptors (Druggability). Available online: https:\/\/github.com\/rodriguez-salarichs\/PockDrug_descriptors."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, S., Xie, J., Pei, J., and Lai, L. (2023). CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases. J. Mol. Biol., 435.","DOI":"10.1016\/j.jmb.2023.168141"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"W374","DOI":"10.1093\/nar\/gky380","article-title":"CavityPlus: A web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction","volume":"46","author":"Xu","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"W611","DOI":"10.1093\/nar\/gkac305","article-title":"ProteinsPlus: A comprehensive collection of web-based molecular modeling tools","volume":"50","author":"Diedrich","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"W48","DOI":"10.1093\/nar\/gkaa235","article-title":"ProteinsPlus: Interactive analysis of protein\u2013ligand binding interfaces","volume":"48","author":"Diedrich","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"W337","DOI":"10.1093\/nar\/gkx333","article-title":"ProteinsPlus: A web portal for structure analysis of macromolecules","volume":"45","author":"Bietz","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_26","unstructured":"Schmidtke, P., Le Guilloux, V., and Shorkar, M. (2024, May 05). fpocket. Available online: https:\/\/github.com\/Discngine\/fpocket."},{"key":"ref_27","unstructured":"(2024, May 05). InDeep. Available online: https:\/\/gitlab.pasteur.fr\/InDeep\/InDeep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1021\/ct501072t","article-title":"A Collective Variable for the Rapid Exploration of Protein Druggability","volume":"11","author":"Cuchillo","year":"2015","journal-title":"J. Chem. Theory Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13321-018-0285-8","article-title":"P2Rank: Machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure","volume":"10","author":"Hoksza","year":"2018","journal-title":"J. Chemin."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"W593","DOI":"10.1093\/nar\/gkac389","article-title":"PrankWeb 3: Accelerated ligand-binding site predictions for experimental and modelled protein structures","volume":"50","author":"Jakubec","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"W345","DOI":"10.1093\/nar\/gkz424","article-title":"PrankWeb: A web server for ligand binding site prediction and visualization","volume":"47","author":"Jendele","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kriv\u00e1k, R., and Hoksza, D. (2015). P2RANK: Knowledge-Based Ligand Binding Site Prediction Using Aggregated Local Features. Algorithms for Computational Biology, Springer.","DOI":"10.1007\/978-3-319-21233-3_4"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13321-015-0059-5","article-title":"Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features","volume":"7","author":"Hoksza","year":"2015","journal-title":"J. Chemin."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1021\/acs.jcim.9b01185","article-title":"Druggability Assessment in TRAPP Using Machine Learning Approaches","volume":"60","author":"Yuan","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kozlovskii, I., and Popov, P. (2020). Spatiotemporal identification of druggable binding sites using deep learning. Commun. Biol., 3.","DOI":"10.1038\/s42003-020-01350-0"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1021\/acs.jcim.1c00475","article-title":"Protein\u2013Peptide Binding Site Detection Using 3D Convolutional Neural Networks","volume":"61","author":"Kozlovskii","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"5069","DOI":"10.1021\/acs.jcim.1c00799","article-title":"DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks","volume":"62","author":"Aggarwal","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btx350","article-title":"DeepSite: Protein-binding site predictor using 3D-convolutional neural networks","volume":"33","author":"Doerr","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1093\/bioinformatics\/btab009","article-title":"DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins","volume":"37","author":"Mylonas","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"W314","DOI":"10.1093\/nar\/gkt503","article-title":"Depth: A web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins","volume":"41","author":"Tan","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, X., Flannery, S.T., and Kihara, D. (2021). Protein Docking Model Evaluation by Graph Neural Networks. Front. Mol. Biosci., 8.","DOI":"10.3389\/fmolb.2021.647915"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1002\/prot.22639","article-title":"Detection of multiscale pockets on protein surfaces using mathematical morphology","volume":"78","author":"Kawabata","year":"2010","journal-title":"Proteins: Struct. Funct. Bioinform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1002\/prot.21732","article-title":"Characterization of local geometry of protein surfaces with the visibility criterion","volume":"71","author":"Li","year":"2008","journal-title":"Proteins Struct. Funct. Bioinform."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Stepniewska-Dziubinska, M.M., Zielenkiewicz, P., and Siedlecki, P. (2020). Improving detection of protein-ligand binding sites with 3D segmentation. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-61860-z"},{"key":"ref_47","unstructured":"Jing, B., Eismann, S., Suriana, P., Townshend, R.J.L., and Dror, R. (2021). Learning from Protein Structure with Geometric Vector Perceptrons. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e1676","DOI":"10.1002\/wcms.1676","article-title":"Ligandability and druggability assessment via machine learning","volume":"13","author":"Abate","year":"2023","journal-title":"WIREs Comput. Mol. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liao, J., Wang, Q., Wu, F., and Huang, Z. (2022). In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets. Molecules, 27.","DOI":"10.3390\/molecules27207103"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.drudis.2016.01.007","article-title":"DrugMiner: Comparative analysis of machine learning algorithms for prediction of potential druggable proteins","volume":"21","author":"Jamali","year":"2016","journal-title":"Drug Discov. Today"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.artmed.2019.07.005","article-title":"Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier","volume":"98","author":"Lin","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gong, Y., Liao, B., Wang, P., and Zou, Q. (2021). DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins. Front. Pharmacol., 12.","DOI":"10.3389\/fphar.2021.771808"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sikander, R., Ghulam, A., and Ali, F. (2022). XGB-DrugPred: Computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-09484-3"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.jare.2022.01.009","article-title":"The applications of deep learning algorithms on in silico druggable proteins identification","volume":"41","author":"Yu","year":"2022","journal-title":"J. Adv. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104883","DOI":"10.1016\/j.isci.2022.104883","article-title":"Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework","volume":"25","author":"Charoenkwan","year":"2022","journal-title":"iScience"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e4555","DOI":"10.1002\/pro.4555","article-title":"QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets","volume":"32","author":"Chen","year":"2023","journal-title":"Protein Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, M., Wan, F., and Liu, T. (2023). DrugFinder: Druggable Protein Identification Model Based on Pre-Trained Models and Evolutionary Information. Algorithms, 16.","DOI":"10.3390\/a16060263"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1007\/s40484-018-0157-2","article-title":"Analysis of protein features and machine learning algorithms for prediction of druggable proteins","volume":"6","author":"Sun","year":"2018","journal-title":"Quant. Biol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Iraji, M.S., Tanha, J., and Habibinejad, M. (2022). Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method. Comput. Biol. Med., 151.","DOI":"10.1016\/j.compbiomed.2022.106276"},{"key":"ref_60","first-page":"915","article-title":"Empirical comparison and analysis of machine learning-based approaches for druggable protein identification","volume":"22","author":"Shoombuatong","year":"2023","journal-title":"EXCLI J."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chilingaryan, G., Abelyan, N., Sargsyan, A., Nazaryan, K., Serobian, A., and Zakaryan, H. (2021). Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-91069-7"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10822-018-0139-5","article-title":"Hybrid receptor structure\/ligand-based docking and activity prediction in ICM: Development and evaluation in D3R Grand Challenge 3","volume":"33","author":"Lam","year":"2019","journal-title":"J. Comput. Mol. Des."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sharo, C., Zhai, T., and Huang, Z. (2024). Investigation of Potential Drug Targets Involved in Inflammation Contributing to Alzheimer\u2019s Disease Progression. Pharmaceuticals, 17.","DOI":"10.3390\/ph17010137"},{"key":"ref_64","unstructured":"(2023, December 10). ICM User\u2019s Guide 5.6 3D Predict. Molsoft, LLC. Available online: https:\/\/molsoft.com\/gui\/3d-predict.html."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s00894-020-04385-6","article-title":"Druggability and drug-likeness concepts in drug design: Are biomodelling and predictive tools having their say?","volume":"26","author":"Agoni","year":"2020","journal-title":"J. Mol. Model."},{"key":"ref_66","unstructured":"(2024, May 05). Target Analysis with SiteMap and WaterMap. Schrodinger. Available online: https:\/\/learn.schrodinger.com\/private\/edu\/release\/current\/Documentation\/html\/tutorials\/target_analysis\/target_analysis.htm."},{"key":"ref_67","unstructured":"Schrodinger (2024, May 05). How SiteMap Evaluates Sites. Available online: https:\/\/learn.schrodinger.com\/private\/edu\/release\/current\/Documentation\/html\/sitemap\/sitemap_user_manual\/sitemap_methodology_evaluate_sites.htm."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1093\/bib\/bbs077","article-title":"Alpha shape and Delaunay triangulation in studies of protein-related interactions","volume":"15","author":"Zhou","year":"2014","journal-title":"Brief. Bioinform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1016\/j.bmcl.2004.01.101","article-title":"Synthesis of a novel peptidic photoaffinity probe for the PTP-1B enzyme","volume":"14","author":"Skorey","year":"2004","journal-title":"Bioorganic Med. Chem. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.jmb.2011.09.031","article-title":"Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology","volume":"414","author":"Fleishman","year":"2011","journal-title":"J. Mol. Biol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1002\/prot.24110","article-title":"Scoring protein interaction decoys using exposed residues (SPIDER): A novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues","volume":"80","author":"Khashan","year":"2012","journal-title":"Proteins Struct. Funct. Bioinform."},{"key":"ref_72","unstructured":"Khashan, R. (2024, May 05). SPIDER\u2014About. Available online: https:\/\/www.bioinformatics.org\/spider\/."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.ifacol.2023.01.009","article-title":"Identification of Apolipoprotein E4 inhibitors for Alzheimer\u2019s Disease Therapy through a Large-Scale Virtual Screening","volume":"55","author":"Krass","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Zhang, F., Graham, J., Zhai, T., Liu, Y., and Huang, Z. (2022). Discovery of MurA Inhibitors as Novel Antimicrobials through an Integrated Computational and Experimental Approach. Antibiotics, 11.","DOI":"10.3390\/antibiotics11040528"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Zhai, T., Zhang, F., Haider, S., Kraut, D., and Huang, Z. (2021). An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease. Front. Mol. Biosci., 8.","DOI":"10.3389\/fmolb.2021.661424"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"7537","DOI":"10.1021\/acsomega.0c00352","article-title":"Synergistic Effect of Chlorogenic Acid and Caffeic Acid with Fosfomycin on Growth Inhibition of a Resistant Listeria monocytogenes Strain","volume":"5","author":"Zhang","year":"2020","journal-title":"ACS Omega"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3233\/JAD-230078","article-title":"Aberrant Expression of GABA-Related Genes in the Hippocampus of 3xTg-AD Model Mice from the Early to End Stages of Alzheimer\u2019s Disease","volume":"94","author":"Mori","year":"2023","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"7505","DOI":"10.1523\/JNEUROSCI.20-20-07505.2000","article-title":"Deficiency of Complement Defense Protein CD59 May Contribute to Neurodegeneration in Alzheimer\u2019s Disease","volume":"20","author":"Yang","year":"2000","journal-title":"J. Neurosci."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Shah, A., Kishore, U., and Shastri, A. (2021). Complement System in Alzheimer\u2019s Disease. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222413647"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Jordan, K.L., Koss, D.J., Outeiro, T.F., and Giorgini, F. (2022). Therapeutic Targeting of Rab GTPases: Relevance for Alzheimer\u2019s Disease. Biomedicines, 10.","DOI":"10.3390\/biomedicines10051141"},{"key":"ref_81","unstructured":"National Center for Biotechnology Information (2024, May 08). PubChem Compound Summary for CID 62959, Trovafloxcin, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/Trovafloxacin."},{"key":"ref_82","unstructured":"DRUGBANK Online (2024, May 08). Trovafloxacin. Available online: https:\/\/go.drugbank.com\/drugs\/DB00685."},{"key":"ref_83","unstructured":"ChEBI (2024, May 08). CHEBI:9763\u2014Trovafloxacin. Available online: https:\/\/www.ebi.ac.uk\/chebi\/searchId.do?chebiId=CHEBI:9763."},{"key":"ref_84","unstructured":"(2024, May 08). DRUGBANK Online.Betrixaban. Available online: https:\/\/go.drugbank.com\/drugs\/DB12364."},{"key":"ref_85","unstructured":"National Center for Biotechnology (2024, May 08). PubChem Compound Summary for CID 10275777, Betrixaban, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/Betrixaban."},{"key":"ref_86","unstructured":"DRUGBANK Online (2024, May 08). Favipiravir. Available online: https:\/\/go.drugbank.com\/drugs\/DB12466."},{"key":"ref_87","unstructured":"(2024, October 15). PubChem Compound Summary for CID 90311989. Bictegravir. National Center for Biotechnology Information, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/90311989."},{"key":"ref_88","unstructured":"National Center for Biotechnology (2024, May 08). PubChem Compound Summary for CID 5339, Sulfasalazine, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/Sulfasalazine."},{"key":"ref_89","unstructured":"National Center for Biotechnology Information (2024, May 08). PubChem Compound Summary for CID 1983, Acetaminophen, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/Acetaminophen."},{"key":"ref_90","unstructured":"Shaughnessy, A. (2022). How Does Acetaminophen Work?, Tufts University School of Medicine."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1039\/C5TA05025A","article-title":"Nanoporous networks as effective stabilisation matrices for nanoscale zero-valent iron and groundwater pollutant removal","volume":"4","author":"Mines","year":"2016","journal-title":"J. Mater. Chem. A"},{"key":"ref_92","unstructured":"National Center for Biotechnology Information (2024, October 15). PubChem Compound Summary for CID 5460875, Asparaginate, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/5460875."},{"key":"ref_93","unstructured":"National Center for Biotechnology Information (2024, October 15). PubChem Compound Summary for CID 6267, Asparagine, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/6267."},{"key":"ref_94","unstructured":"National Center for Biotechnology Information (2024, October 15). PubChem Compound Summary for CID 3830, Kinetin, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/3830."},{"key":"ref_95","unstructured":"National Center for Biotechnology Inofrmation (2024, October 15). PubChem Compound Summary for CID 151075, Nepafenac, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/compound\/151075."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"277","DOI":"10.4103\/0301-4738.98705","article-title":"Effect of preoperative use of topical prednisolone acetate, ketorolac tromethamine, nepafenac and placebo, on the maintenance of intraoperative mydriasis during cataract surgery: A randomized trial","volume":"60","author":"Lira","year":"2012","journal-title":"Indian J. Ophthalmol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1016\/j.cell.2007.10.036","article-title":"The Classical Complement Cascade Mediates CNS Synapse Elimination","volume":"131","author":"Stevens","year":"2007","journal-title":"Cell"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"10016","DOI":"10.1073\/pnas.89.21.10016","article-title":"Complement activation by beta-amyloid in Alzheimer disease","volume":"89","author":"Rogers","year":"1992","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/0169-328X(92)90017-6","article-title":"Complement gene expression in human brain: Comparison between normal and Alzheimer disease cases","volume":"14","author":"Walker","year":"1992","journal-title":"Mol. Brain Res."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Batista, A.F., Khan, K.A., Papavergi, M.-T., and Lemere, C.A. (2024). The Importance of Complement-Mediated Immune Signaling in Alzheimer\u2019s Disease Pathogenesis. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25020817"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1126\/science.aad8373","article-title":"Complement and microglia mediate early synapse loss in Alzheimer mouse models","volume":"352","author":"Hong","year":"2016","journal-title":"Science"}],"container-title":["Targets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2813-3137\/2\/4\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:46:35Z","timestamp":1760114795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2813-3137\/2\/4\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":101,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["targets2040025"],"URL":"https:\/\/doi.org\/10.3390\/targets2040025","relation":{},"ISSN":["2813-3137"],"issn-type":[{"value":"2813-3137","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]}}}