{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:54:04Z","timestamp":1764240844151,"version":"3.40.4"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biophysical Chemistry"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.bpc.2025.107434","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T16:17:05Z","timestamp":1741623425000},"page":"107434","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Autoencoder-based drug-virus association prediction with reliable negative sample selection: A case study with COVID-19"],"prefix":"10.1016","volume":"322","author":[{"given":"A.S.","family":"Aruna","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.R.","family":"Remesh Babu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Deepthi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.bpc.2025.107434_bb0005","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11481-020-09944-5","article-title":"The natural history, pathobiology, and clinical manifestations of SARS-CoV-2 infections","volume":"15","author":"Machhi","year":"2020","journal-title":"J. Neuroimmune Pharmacol."},{"key":"10.1016\/j.bpc.2025.107434_bb0010","article-title":"Recent trends in analytical and digital techniques for the detection of the SARS-Cov-2","volume":"270","author":"Mukhopadhyay","year":"2021","journal-title":"Biophys. Chem."},{"issue":"suppl","key":"10.1016\/j.bpc.2025.107434_bb0015","first-page":"6","article-title":"Emerging coronaviruses: first SARS, second MERS and third SARS-CoV-2: epidemiological updates of COVID-19","volume":"28","author":"Halaji","year":"2020","journal-title":"Infez. Med."},{"issue":"4","key":"10.1016\/j.bpc.2025.107434_bb0020","doi-asserted-by":"crossref","first-page":"821","DOI":"10.3390\/cells10040821","article-title":"Role of structural and non-structural proteins and therapeutic targets of SARS-CoV-2 for COVID-19","volume":"10","author":"Yadav","year":"2021","journal-title":"Cells"},{"key":"10.1016\/j.bpc.2025.107434_bb0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.bpc.2023.107013","article-title":"Potential differentiation of successive SARS-CoV-2 mutations by RNA: DNA hybrid analyses","volume":"297","author":"He","year":"2023","journal-title":"Biophys. Chem."},{"issue":"17","key":"10.1016\/j.bpc.2025.107434_bb0030","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1111\/febs.15375","article-title":"A guide to COVID-19: a global pandemic caused by the novel coronavirus SARS-CoV-2","volume":"287","author":"Atzrodt","year":"2020","journal-title":"FEBS J."},{"key":"10.1016\/j.bpc.2025.107434_bb0035","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial intelligence to deep learning: machine intelligence approach for drug discovery","volume":"25","author":"Gupta","year":"2021","journal-title":"Mol. Divers."},{"issue":"16","key":"10.1016\/j.bpc.2025.107434_bb0040","doi-asserted-by":"crossref","first-page":"9121","DOI":"10.1039\/D0CS01065K","article-title":"A critical overview of computational approaches employed for COVID-19 drug discovery","volume":"50","author":"Muratov","year":"2021","journal-title":"Chem. Soc. Rev."},{"key":"10.1016\/j.bpc.2025.107434_bb0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107135","article-title":"Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study","volume":"103","author":"Meng","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.bpc.2025.107434_bb0050","series-title":"Machine Learning, Optimization, and Data Science: 8th International Workshop, LOD 2022, Certosa di Pontignano, Italy, September 19\u201322, 2022, Revised Selected Papers, Part I","first-page":"35","article-title":"A matrix factorization-based drug-virus link prediction method for SARS-CoV-2 drug prioritization","author":"Li","year":"2023"},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0055","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1038\/s41598-021-83737-5","article-title":"Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures","volume":"11","author":"Peng","year":"2021","journal-title":"Sci. Rep."},{"issue":"13","key":"10.1016\/j.bpc.2025.107434_bb0060","doi-asserted-by":"crossref","first-page":"3772","DOI":"10.1111\/jcmm.17412","article-title":"Computational drug repositioning using similarity constrained weight regularization matrix factorization: a case of COVID-19","volume":"26","author":"Xu","year":"2022","journal-title":"J. Cell. Mol. Med."},{"key":"10.1016\/j.bpc.2025.107434_bb0065","doi-asserted-by":"crossref","unstructured":"A S Aruna, K R Remesh Babu, K Deepthi, an ensemble approach to prioritize antivirals against COVID-19 via heterogeneous network inference-based inductive matrix completion, Journal of Computational Biophysics and Chemistry, doi: https:\/\/doi.org\/10.1142\/S2737416523410041.","DOI":"10.1142\/S2737416523410041"},{"key":"10.1016\/j.bpc.2025.107434_bb0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105119","article-title":"VDA-RWLRLS: an anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares","volume":"140","author":"Shen","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bpc.2025.107434_bb0075","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2020.603615","article-title":"Indicator regularized non-negative matrix factorization method-based drug repurposing for COVID-19","volume":"11","author":"Tang","year":"2021","journal-title":"Front. Immunol."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0080","doi-asserted-by":"crossref","first-page":"9047","DOI":"10.1038\/s41598-021-88153-3","article-title":"A computational approach to aid clinicians in selecting antiviral drugs for COVID-19 trials","volume":"11","author":"Mongia","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bpc.2025.107434_bb0085","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ymeth.2023.10.002","article-title":"Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning","volume":"219","author":"Ma","year":"2023","journal-title":"Methods"},{"key":"10.1016\/j.bpc.2025.107434_bb0090","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2022.740382","article-title":"Discovery of potential therapeutic drugs for covid-19 through logistic matrix factorization with kernel diffusion","volume":"13","author":"Tian","year":"2022","journal-title":"Front. Microbiol."},{"issue":"6","key":"10.1016\/j.bpc.2025.107434_bb0095","doi-asserted-by":"crossref","first-page":"4427","DOI":"10.1016\/j.ygeno.2020.07.044","article-title":"Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method","volume":"112","author":"Zhou","year":"2020","journal-title":"Genomics"},{"key":"10.1016\/j.bpc.2025.107434_bb0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.110059","article-title":"Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review","volume":"139","author":"Lalmuanawma","year":"2020","journal-title":"Chaos, Solitons Fractals"},{"key":"10.1016\/j.bpc.2025.107434_bb0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108487","article-title":"DeepSeq2Drug: an expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning","volume":"175","author":"Xie","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bpc.2025.107434_bb0110","article-title":"A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing","volume":"113","author":"Deepthi","year":"2021","journal-title":"Appl. Soft Comput."},{"issue":"Supplement_1","key":"10.1016\/j.bpc.2025.107434_bb0115","doi-asserted-by":"crossref","first-page":"i475","DOI":"10.1093\/bioinformatics\/btad244","article-title":"DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response","volume":"39","author":"Huang","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bpc.2025.107434_bb0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107831","article-title":"SANE: a sequence combined attentive network embedding model for COVID-19 drug repositioning","volume":"111","author":"Su","year":"2021","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0125","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s12859-023-05181-8","article-title":"DRaW: prediction of COVID-19 antivirals by deep learning\u2014an objection on using matrix factorization","volume":"24","author":"Hashemi","year":"2023","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0130","doi-asserted-by":"crossref","first-page":"23179","DOI":"10.1038\/s41598-021-02353-5","article-title":"Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence","volume":"11","author":"Hsieh","year":"2021","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0135","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3390\/fi13010013","article-title":"Knowledge-graph-based drug repositioning against COVID-19 by graph convolutional network with attention mechanism","volume":"13","author":"Che","year":"2021","journal-title":"Future Internet"},{"key":"10.1016\/j.bpc.2025.107434_bb0140","first-page":"1","article-title":"A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study","author":"Aruna","year":"2024","journal-title":"Mol. Divers."},{"key":"10.1016\/j.bpc.2025.107434_bb0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2021.103696","article-title":"Drug repurposing for COVID-19 via knowledge graph completion","volume":"115","author":"Zhang","year":"2021","journal-title":"J. Biomed. Inform."},{"issue":"19","key":"10.1016\/j.bpc.2025.107434_bb0150","doi-asserted-by":"crossref","first-page":"4918","DOI":"10.1093\/bioinformatics\/btaa598","article-title":"Predicting human microbe\u2013drug associations via graph convolutional network with conditional random field","volume":"36","author":"Long","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bpc.2025.107434_bb0155","series-title":"International Conference on Communication and Intelligent Systems","first-page":"267","article-title":"A drug-virus prediction model based on an ensemble classifier with feature optimization: A case study with COVID-19","author":"Aruna","year":"2023"},{"key":"10.1016\/j.bpc.2025.107434_bb0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2022.104640","article-title":"A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2","volume":"229","author":"Das","year":"2022","journal-title":"Chemom. Intel. Lab. Syst."},{"issue":"D1","key":"10.1016\/j.bpc.2025.107434_bb0165","doi-asserted-by":"crossref","first-page":"D1091","DOI":"10.1093\/nar\/gkt1068","article-title":"DrugBank 4.0: shedding new light on drug mefigolism","volume":"42","author":"Law","year":"2014","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0170","first-page":"1","article-title":"Open babel: an open chemical toolbox","volume":"3","author":"O\u2019Boyle","year":"2011","journal-title":"J. Chem."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0175","first-page":"1","article-title":"Why is Tanimoto index an appropriate choice for fingerprintbased similarity calculations?","volume":"7","author":"Bajusz","year":"2015","journal-title":"J. Chem."},{"issue":"D1","key":"10.1016\/j.bpc.2025.107434_bb0180","doi-asserted-by":"crossref","first-page":"D29","DOI":"10.1093\/nar\/gkac1032","article-title":"Database resources of the National Center for biotechnology information in 2023","volume":"51","author":"Sayers","year":"2023","journal-title":"Nucleic Acids Res."},{"issue":"4","key":"10.1016\/j.bpc.2025.107434_bb0185","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1093\/molbev\/mst010","article-title":"MAFFT multiple sequence alignment software version 7: improvements in performance and usability","volume":"30","author":"Katoh","year":"2013","journal-title":"Mol. Biol. Evol."},{"key":"10.1016\/j.bpc.2025.107434_bb0190","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.ijid.2020.02.018","article-title":"Discovery and development of safe-in-man broad-spectrum antiviral agents","volume":"93","author":"Andersen","year":"2020","journal-title":"Int. J. Infect. Dis."},{"issue":"5","key":"10.1016\/j.bpc.2025.107434_bb0195","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."},{"year":"2016","series-title":"Rdkit: Open-Source Cheminformatics Software","author":"Landrum","key":"10.1016\/j.bpc.2025.107434_bb0200"},{"issue":"24","key":"10.1016\/j.bpc.2025.107434_bb0205","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","article-title":"Predicting miRNA\u2013disease association based on inductive matrix completion","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bpc.2025.107434_bb0210","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.ins.2023.01.112","article-title":"Sure: screening unlabeled samples for reliable negative samples based on reinforcement learning","volume":"629","author":"Li","year":"2023","journal-title":"Inform. Sci."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0215","doi-asserted-by":"crossref","first-page":"bbab461","DOI":"10.1093\/bib\/bbab461","article-title":"Positive-unlabeled learning in bioinformatics and computational biology: a brief review","volume":"23","author":"Li","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.bpc.2025.107434_bb0220","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"Peterson","year":"2009","journal-title":"K-nearest neighbor. Scholarpedia"},{"key":"10.1016\/j.bpc.2025.107434_bb0225","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s40291-020-00499-y","article-title":"Inferring potential CircRNA\u2013disease associations via deep autoencoder-based classification","volume":"25","author":"Deepthi","year":"2021","journal-title":"Mol. Diagn. Ther."},{"key":"10.1016\/j.bpc.2025.107434_bb0230","series-title":"Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics","first-page":"533","article-title":"Deep autoencoder neural networks for gene ontology annotation predictions","author":"Chicco","year":"2014"},{"key":"10.1016\/j.bpc.2025.107434_bb0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.gene.2020.145040","article-title":"An ensemble approach for CircRNA-disease association prediction based on autoencoder and deep neural network","volume":"762","author":"Deepthi","year":"2020","journal-title":"Gene"},{"key":"10.1016\/j.bpc.2025.107434_bb0240","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016, August). Xgboost: a scalable tree boosting system. In Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining (pp. 785-794).","DOI":"10.1145\/2939672.2939785"},{"issue":"4","key":"10.1016\/j.bpc.2025.107434_bb0245","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1093\/bioinformatics\/btz734","article-title":"SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting","volume":"36","author":"Yu","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0250","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41419-017-0003-x","article-title":"EGBMMDA: extreme gradient boosting machine for MiRNA-disease association prediction","volume":"9","author":"Chen","year":"2018","journal-title":"Cell Death Dis."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0255","doi-asserted-by":"crossref","first-page":"5505","DOI":"10.1038\/s41598-022-09484-3","article-title":"XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set","volume":"12","author":"Sikander","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bpc.2025.107434_bb0260","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-03721-0","article-title":"LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting","volume":"21","author":"Zhang","year":"2020","journal-title":"BMC bioinformatics"},{"key":"10.1016\/j.bpc.2025.107434_bb0265","series-title":"Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23\u201327, 2013, Proceedings, Part III 13","first-page":"451","article-title":"Area under the precision-recall curve: point estimates and confidence intervals","author":"Boyd","year":"2013"},{"issue":"9","key":"10.1016\/j.bpc.2025.107434_bb0270","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","article-title":"Receiver operating characteristic curve in diagnostic test assessment","volume":"5","author":"Mandrekar","year":"2010","journal-title":"J. Thorac. Oncol."},{"key":"10.1016\/j.bpc.2025.107434_bb0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.lfs.2020.117592","article-title":"Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir against SARS-CoV-2 RNA dependent RNA polymerase (RdRp): a molecular docking study","volume":"253","author":"Elfiky","year":"2020","journal-title":"Life Sci."},{"issue":"19","key":"10.1016\/j.bpc.2025.107434_bb0280","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1056\/NEJMoa2007764","article-title":"Remdesivir for the treatment of Covid-19","volume":"383","author":"Beigel","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.bpc.2025.107434_bb0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.lfs.2020.117775","article-title":"A systematic review on use of aminoquinolines for the therapeutic management of COVID-19: efficacy, safety and clinical trials","volume":"254","author":"Patil","year":"2020","journal-title":"Life Sci."},{"issue":"9","key":"10.1016\/j.bpc.2025.107434_bb0290","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1111\/1348-0421.12828","article-title":"Antiviral activities of mycophenolic acid and IMD-0354 against SARS-CoV-2","volume":"64","author":"Kato","year":"2020","journal-title":"Microbiol. Immunol."},{"key":"10.1016\/j.bpc.2025.107434_bb0295","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.ijid.2020.10.069","article-title":"Role of favipiravir in the treatment of COVID-19","volume":"102","author":"Joshi","year":"2021","journal-title":"Int. J. Infect. Dis."},{"issue":"13","key":"10.1016\/j.bpc.2025.107434_bb0300","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.1111\/bph.15843","article-title":"Niclosamide\u2014a promising treatment for COVID-19","volume":"179","author":"Singh","year":"2022","journal-title":"Br. J. Pharmacol."},{"key":"10.1016\/j.bpc.2025.107434_bb0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.bioorg.2020.104257","article-title":"Is oseltamivir suitable for fighting against COVID-19: in silico assessment, in vitro and retrospective study","volume":"104","author":"Tan","year":"2020","journal-title":"Bioorg. Chem."},{"issue":"3","key":"10.1016\/j.bpc.2025.107434_bb0310","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3390\/ph13030051","article-title":"Emetine, ipecac, ipecac alkaloids and analogues as potential antiviral agents for coronaviruses","volume":"13","author":"Bleasel","year":"2020","journal-title":"Pharmaceuticals"},{"issue":"11","key":"10.1016\/j.bpc.2025.107434_bb0315","doi-asserted-by":"crossref","first-page":"11169","DOI":"10.1007\/s11033-022-07822-2","article-title":"Nitazoxanide and COVID-19: a review","volume":"49","author":"Al-Kuraishy","year":"2022","journal-title":"Mol. Biol. Rep."},{"issue":"1","key":"10.1016\/j.bpc.2025.107434_bb0320","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."},{"issue":"16","key":"10.1016\/j.bpc.2025.107434_bb0325","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."}],"container-title":["Biophysical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301462225000468?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301462225000468?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T11:43:59Z","timestamp":1745408639000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0301462225000468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":65,"alternative-id":["S0301462225000468"],"URL":"https:\/\/doi.org\/10.1016\/j.bpc.2025.107434","relation":{},"ISSN":["0301-4622"],"issn-type":[{"type":"print","value":"0301-4622"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Autoencoder-based drug-virus association prediction with reliable negative sample selection: A case study with COVID-19","name":"articletitle","label":"Article Title"},{"value":"Biophysical Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bpc.2025.107434","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"107434"}}