{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:19:05Z","timestamp":1760746745038,"version":"build-2065373602"},"reference-count":70,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322301","62272004","62172002"],"award-info":[{"award-number":["62322301","62272004","62172002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In recent years, antimicrobial peptides (AMPs) have attracted interest as potential peptide antibiotic due to their broad-spectrum antibacterial activity and high target specificity. However, existing research on AMP prediction mainly focuses on their functional properties, such as antibacterial, antiviral, and anticancer. This emphasis has created a significant gap in identifying AMPs that specifically target pathogens. Given the large variety of pathogens and the sparsity and imbalance of labels, it is challenging to determine which specific pathogens AMPs can effective against. To address this issue, we present PepXML, a large language model-based tool for extreme multilabel classification of pathogen-targeted AMPs. Our first step involved constructing a benchmark dataset of AMPs and their corresponding targeted pathogens, sourced from public databases. In PepXML, the peptides are embedded using ESM2. Further, clustering on a specifically designed label co-occurrence graph and hard negative sampling were employed to address challenges on data sparsity and label imbalance. To validate the reliability of our predictive results, we conducted molecular docking studies focused on peptide-bilayer membrane interactions and performed molecular dynamics simulations to elucidate the mechanisms of peptide-pathogen interactions. We anticipate that PepXML will be a valuable resource for advancing peptide-based therapeutics. The data and Python codes of the PepXML model are available at https:\/\/github.com\/YannanBin\/PepXML.git.<\/jats:p>","DOI":"10.1093\/bib\/bbaf548","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T11:05:08Z","timestamp":1760699108000},"source":"Crossref","is-referenced-by-count":0,"title":["Pepxml: ESM2-based extreme multilabel classification of pathogen-targeted antimicrobial peptides"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-5930","authenticated-orcid":false,"given":"Yannan","family":"Bin","sequence":"first","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Life Sciences and Medical Engineering, Anhui University , Jiulong Road 111#, Hefei, Anhui 230601 ,","place":["China"]}]},{"given":"Daijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Life Sciences and Medical Engineering, Anhui University , Jiulong Road 111#, Hefei, Anhui 230601 ,","place":["China"]}]},{"given":"Zhiyang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology , Luoyu Road 1037#, Wuhan, Hubei 430070 ,","place":["China"]}]},{"given":"Chungui","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Institute of Orthopedics, Research Center for Translational Medicine, the Second Affiliated Hospital of Anhui Medical University , Furong Road 678#, Hefei, Anhui 230601 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-7133","authenticated-orcid":false,"given":"Yansen","family":"Su","sequence":"additional","affiliation":[{"name":"School of Internet, Anhui University , Jiulong Road 111# Hefei, Anhui 230601 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"2025101707050317900_ref1","doi-asserted-by":"publisher","first-page":"2857","DOI":"10.1080\/10408398.2015.1077192","article-title":"Antimicrobial resistance: a global emerging threat to public health systems","volume":"57","author":"Ferri","year":"2017","journal-title":"Crit Rev Food Sci"},{"key":"2025101707050317900_ref2","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.3390\/healthcare11131946","article-title":"Antimicrobial resistance: a growing serious threat for global public health","volume":"11","author":"Salam","year":"2023","journal-title":"Health"},{"key":"2025101707050317900_ref3","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/s41586-023-06887-8","article-title":"Discovery of a structural class of antibiotics with explainable deep learning","volume":"626","author":"Wong","year":"2023","journal-title":"Nature"},{"key":"2025101707050317900_ref4","first-page":"3919","article-title":"The antimicrobial peptides and their potential clinical applications","volume":"11","author":"Lei","year":"2019","journal-title":"Am J Transl Res"},{"key":"2025101707050317900_ref5","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1002\/med.21879","article-title":"Antimicrobial peptides, conventional antibiotics, and their synergistic utility for the treatment of drug-resistant infections","volume":"42","author":"Zhu","year":"2022","journal-title":"Med Res Rev"},{"key":"2025101707050317900_ref6","doi-asserted-by":"publisher","first-page":"100954","DOI":"10.1016\/j.drup.2023.100954","article-title":"Antimicrobial peptides for combating drug-resistant bacterial infections","volume":"68","author":"Xuan","year":"2023","journal-title":"Drug Resist Updat"},{"key":"2025101707050317900_ref7","doi-asserted-by":"publisher","first-page":"15519","DOI":"10.1021\/jacs.2c03858","article-title":"Pseudo-isolated \u03b1-helix platform for the recognition of deep and narrow targets","volume":"144","author":"Kim","year":"2022","journal-title":"J Am Chem Soc"},{"key":"2025101707050317900_ref8","doi-asserted-by":"publisher","first-page":"118407","DOI":"10.1016\/j.lfs.2020.118407","article-title":"Antimicrobial peptides-advances in development of therapeutic applications","volume":"260","author":"Huy Xuan","year":"2020","journal-title":"Life Sci"},{"key":"2025101707050317900_ref9","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41573-020-00135-8","article-title":"Trends in peptide drug discovery","volume":"20","author":"Muttenthaler","year":"2021","journal-title":"Nat Rev Drug Discov"},{"key":"2025101707050317900_ref10","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s00044-024-03269-1","article-title":"Peptide-based therapeutics: challenges and solutions","volume":"33","author":"Pereira","year":"2024","journal-title":"Med Chem Res"},{"key":"2025101707050317900_ref11","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s11671-025-04224-8","article-title":"Stigmurin encapsulated PLA\u2013PEG ameliorates its therapeutic potential, antimicrobial and antiproliferative activities","volume":"20","author":"Thakur","year":"2025","journal-title":"Discover Nano"},{"key":"2025101707050317900_ref12","doi-asserted-by":"publisher","first-page":"e2401793","DOI":"10.1002\/advs.202401793","article-title":"Dual-mechanism peptide SR25 has broad antimicrobial activity and potential application for healing bacteria-infected diabetic wounds","volume":"11","author":"Luo","year":"2024","journal-title":"Adv Sci"},{"key":"2025101707050317900_ref13","doi-asserted-by":"publisher","first-page":"100034","DOI":"10.1016\/j.fsirep.2021.100034","article-title":"BigPEN, an antimicrobial peptide of penaeidin family from shrimp Litopenaeus vannamei with membrane permeable and DNA binding activity","volume":"2","author":"Xiao","year":"2021","journal-title":"Fish Shellfish Immun Rep"},{"key":"2025101707050317900_ref14","doi-asserted-by":"publisher","first-page":"bbab209","DOI":"10.1093\/bib\/bbab209","article-title":"iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for dentifying antimicrobial peptides and their functional types","volume":"22","author":"Xiao","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref15","doi-asserted-by":"publisher","first-page":"bbad443","DOI":"10.1093\/bib\/bbad443","article-title":"iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-attention combination model","volume":"25","author":"Xing","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref16","doi-asserted-by":"publisher","first-page":"bbad240","DOI":"10.1093\/bib\/bbad240","article-title":"iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities","volume":"24","author":"Xu","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref17","doi-asserted-by":"publisher","first-page":"e12283","DOI":"10.1016\/j.heliyon.2022.e12283","article-title":"Peptide utility (PU) search server: a new tool for peptide sequence search from multiple databases","volume":"8","author":"Chamoli","year":"2022","journal-title":"Heliyon"},{"key":"2025101707050317900_ref18","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1038\/s41598-018-19752-w","article-title":"AmPEP: sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest","volume":"8","author":"Bhadra","year":"2018","journal-title":"Sci Rep"},{"key":"2025101707050317900_ref19","doi-asserted-by":"publisher","first-page":"btac715","DOI":"10.1093\/bioinformatics\/btac715","article-title":"sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure","volume":"39","author":"Yan","year":"2023","journal-title":"Bioinformatics"},{"key":"2025101707050317900_ref20","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1002\/prot.26681","article-title":"sAMP-VGG16: force-field assisted image-based deep neural network prediction model for short antimicrobial peptides","volume":"93","author":"Pandey","year":"2025","journal-title":"Proteins"},{"key":"2025101707050317900_ref21","doi-asserted-by":"publisher","first-page":"btad334","DOI":"10.1093\/bioinformatics\/btad334","article-title":"Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function","volume":"39","author":"Fan","year":"2023","journal-title":"Bioinformatics"},{"key":"2025101707050317900_ref22","doi-asserted-by":"publisher","first-page":"e1010511","DOI":"10.1371\/journal.pcbi.1010511","article-title":"PrMFTP: multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization","volume":"18","author":"Yan","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2025101707050317900_ref23","doi-asserted-by":"publisher","first-page":"bbad353","DOI":"10.1093\/bib\/bbad353","article-title":"FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses","volume":"24","author":"Cao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref24","doi-asserted-by":"publisher","first-page":"bbab414","DOI":"10.1093\/bib\/bbab414","article-title":"Identifying multi-functional bioactive peptide functions using multi-label deep learning","volume":"23","author":"Tang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref25","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/JBHI.2023.3271611","article-title":"Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens","volume":"28","author":"Sharma","year":"2024","journal-title":"IEEE J Biomed Health"},{"key":"2025101707050317900_ref26","doi-asserted-by":"publisher","first-page":"e5006","DOI":"10.1002\/pro.5006","article-title":"AMPActiPred: a three-stage framework for predicting antibacterial peptides and activity levels with deep forest","volume":"33","author":"Yao","year":"2024","journal-title":"Protein Sci"},{"key":"2025101707050317900_ref27","doi-asserted-by":"publisher","first-page":"212105:1","DOI":"10.1007\/s11432-024-4147-8","article-title":"TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning","volume":"67","author":"Yan","year":"2024","journal-title":"SCIENCE CHINA Inf Sci"},{"key":"2025101707050317900_ref28","first-page":"6698","article-title":"BGNN-xml: bilateral graph neural networks for extreme multi-label text classification","volume":"35","author":"Zong","year":"2023","journal-title":"IEEE T Knowl Data En"},{"key":"2025101707050317900_ref29","doi-asserted-by":"publisher","author":"Kharbanda","DOI":"10.1145\/3637528.3672063"},{"key":"2025101707050317900_ref30","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.1007\/s11063-021-10444-7","article-title":"Label-aware document representation via hybrid attention for extreme multi-label text classification","volume":"54","author":"Huang","year":"2022","journal-title":"Neural Process Lett"},{"key":"2025101707050317900_ref31","doi-asserted-by":"publisher","author":"Zhan","DOI":"10.1145\/3404835.3462880"},{"key":"2025101707050317900_ref32","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10994-023-06468-w","article-title":"Meta-classifier free negative sampling for extreme multilabel classification","volume":"113","author":"Qaraei","year":"2024","journal-title":"Mach Learn"},{"key":"2025101707050317900_ref33","doi-asserted-by":"publisher","author":"Dahiya","DOI":"10.1145\/3437963.3441810"},{"key":"2025101707050317900_ref34","doi-asserted-by":"publisher","first-page":"D288","DOI":"10.1093\/nar\/gkaa991","article-title":"DBAASP v3: database of antimicrobial\/cytotoxic activity and structure of peptides as a resource for development of new therapeutics","volume":"49","author":"Pirtskhalava","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025101707050317900_ref35","doi-asserted-by":"publisher","first-page":"bbad135","DOI":"10.1093\/bib\/bbad135","article-title":"UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity","volume":"24","author":"Du","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref36","doi-asserted-by":"publisher","author":"Grover","DOI":"10.1145\/2939672.2939754"},{"key":"2025101707050317900_ref37","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1016\/0960-9822(93)90036-N","article-title":"Why species and subspecies?","volume":"3","author":"Barton","year":"1993","journal-title":"Curr Biol"},{"key":"2025101707050317900_ref38","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2025101707050317900_ref39","doi-asserted-by":"publisher","first-page":"3732","DOI":"10.1021\/acs.jproteome.0c00276","article-title":"Prediction of neuropeptides from sequence information using ensemble classifier and hybrid features","volume":"19","author":"Bin","year":"2020","journal-title":"J Proteome Res"},{"key":"2025101707050317900_ref40","doi-asserted-by":"publisher","first-page":"bbae583","DOI":"10.1093\/bib\/bbae583","article-title":"ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information","volume":"25","author":"Yu","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref41","doi-asserted-by":"publisher","first-page":"D377","DOI":"10.1093\/nar\/gkac933","article-title":"CAMPR4: a database of natural and synthetic antimicrobial peptides","volume":"51","author":"Gawde","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025101707050317900_ref42","doi-asserted-by":"publisher","first-page":"107965","DOI":"10.1016\/j.patcog.2021.107965","article-title":"A review of methods for imbalanced multi-label classification","volume":"118","author":"Tarekegn","year":"2021","journal-title":"Pattern Recogn"},{"key":"2025101707050317900_ref43","doi-asserted-by":"publisher","first-page":"108408","DOI":"10.1016\/j.compbiomed.2024.108408","article-title":"Sa-TTCA: an SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing","volume":"174","author":"Tran","year":"2024","journal-title":"Comput Biol Med"},{"key":"2025101707050317900_ref44","doi-asserted-by":"publisher","first-page":"e2300011","DOI":"10.1002\/pmic.202300011","article-title":"Leveraging transformers-based language models in proteome bioinformatics","volume":"23","author":"Le","year":"2023","journal-title":"Proteomics"},{"key":"2025101707050317900_ref45","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.3390\/electronics10202470","article-title":"CNN variants for computer vision: history, architecture, application, challenges and future scope","volume":"10","author":"Bhatt","year":"2021","journal-title":"Electronics"},{"key":"2025101707050317900_ref46","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","article-title":"A review on the long short-term memory model","volume":"53","author":"Van Houdt","year":"2020","journal-title":"Artif Intell Rev"},{"key":"2025101707050317900_ref47","first-page":"107547","article-title":"xLSTM: extended long short-term memory","volume":"37","author":"Beck","year":"2024","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2025101707050317900_ref48","author":"Vaswani"},{"key":"2025101707050317900_ref49","doi-asserted-by":"publisher","author":"Peng","DOI":"10.1145\/3627673.3680276"},{"key":"2025101707050317900_ref50","doi-asserted-by":"publisher","first-page":"7407","DOI":"10.1038\/s41467-024-51844-2","article-title":"Fine-tuning protein language models boosts predictions across diverse tasks","volume":"15","author":"Schmirler","year":"2024","journal-title":"Nat Commun"},{"key":"2025101707050317900_ref51","doi-asserted-by":"publisher","first-page":"e4928","DOI":"10.1002\/pro.4928","article-title":"Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow","volume":"33","author":"Martinez-Mauricio","year":"2024","journal-title":"Protein Sci"},{"key":"2025101707050317900_ref52","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","article-title":"The k-means algorithm: a comprehensive survey and performance evaluation","volume":"9","author":"Ahmed","year":"2020","journal-title":"Electronics"},{"key":"2025101707050317900_ref53","doi-asserted-by":"publisher","author":"Nayak","DOI":"10.1007\/978-81-322-2208-8_14"},{"key":"2025101707050317900_ref54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3408318","article-title":"Gaussian mixture model clustering with incomplete data","volume":"17","author":"Zhang","year":"2021","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"2025101707050317900_ref55","first-page":"86","article-title":"Algorithms for hierarchical clustering: an overview","volume":"2","author":"Murtagh","year":"2012","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"2025101707050317900_ref56","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s11063-024-11460-z","article-title":"TLC-xml: transformer with label correlation for extreme multi-label text classification","volume":"56","author":"Zhao","year":"2024","journal-title":"Neural Process Lett"},{"key":"2025101707050317900_ref57","doi-asserted-by":"publisher","first-page":"110342","DOI":"10.1016\/j.patcog.2024.110342","article-title":"A thorough experimental comparison of multilabel methods for classification performance","volume":"151","author":"Garc\u00eda-Pedrajas","year":"2024","journal-title":"Pattern Recogn"},{"key":"2025101707050317900_ref58","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-label scene classification","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recogn"},{"key":"2025101707050317900_ref59","doi-asserted-by":"publisher","first-page":"bbac094","DOI":"10.1093\/bib\/bbac094","article-title":"Do deep learning models make a difference in the identification of antimicrobial peptides?","volume":"23","author":"Garc\u00eda-Jacas","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref60","doi-asserted-by":"publisher","first-page":"bbac428","DOI":"10.1093\/bib\/bbac428","article-title":"Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant?","volume":"23","author":"Garc\u00eda-Jacas","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025101707050317900_ref61","doi-asserted-by":"publisher","author":"Coates","DOI":"10.1007\/978-3-642-35289-8_30"},{"key":"2025101707050317900_ref62","doi-asserted-by":"publisher","author":"Cohen-Addad","DOI":"10.1145\/3519935.3520011"},{"key":"2025101707050317900_ref63","doi-asserted-by":"publisher","first-page":"5368","DOI":"10.1093\/bioinformatics\/btac711","article-title":"Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities","volume":"38","author":"Pang","year":"2022","journal-title":"Bioinformatics"},{"key":"2025101707050317900_ref64","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s12602-024-10261-z","article-title":"Peptide-membrane docking and molecular dynamic simulation of in silico detected antimicrobial peptides from Portulaca oleracea\u2019s transcriptome","volume":"16","author":"Hasannejad-Asl","year":"2024","journal-title":"Probiotics Antimicro"},{"key":"2025101707050317900_ref65","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1021\/acsomega.0c04752","article-title":"Molecular dynamics simulation of the interaction of two linear battacin analogs with model gram-positive and gram-negative bacterial cell membranes","volume":"6","author":"Chakraborty","year":"2020","journal-title":"ACS Omega"},{"key":"2025101707050317900_ref66","doi-asserted-by":"publisher","first-page":"6838","DOI":"10.1021\/acs.jcim.4c01100","article-title":"Synergistic antimicrobial mechanism of the ultrashort antimicrobial peptide R3W4V with a tadpole-like conformation","volume":"64","author":"Cao","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2025101707050317900_ref67","doi-asserted-by":"publisher","first-page":"W449","DOI":"10.1093\/nar\/gkw329","article-title":"PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex","volume":"44","author":"Lamiable","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2025101707050317900_ref68","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1002\/jcc.20945","article-title":"CHARMM-GUI: a web-based graphical user interface for CHARMM","volume":"29","author":"Jo","year":"2008","journal-title":"J Comput Chem"},{"key":"2025101707050317900_ref69","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.softx.2015.06.001","article-title":"GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers","volume":"1-2","author":"Abraham","year":"2015","journal-title":"SoftwareX"},{"key":"2025101707050317900_ref70","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/0263-7855(96)00018-5","article-title":"VMD: visual molecular dynamics","volume":"14","author":"Humphrey","year":"1996","journal-title":"J Mol Graph"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/5\/bbaf548\/64729845\/bbaf548.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/5\/bbaf548\/64729845\/bbaf548.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T11:05:10Z","timestamp":1760699110000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf548\/8290423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"references-count":70,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,8,31]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf548","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,9]]},"published":{"date-parts":[[2025,8,31]]},"article-number":"bbaf548"}}