[1]

d'Ayala GG, Malinconico M, Laurienzo P. 2008. Marine derived polysaccharides for biomedical applications: chemical modification approaches. Molecules 13:2069−2106

doi: 10.3390/molecules13092069
[2]

BeMiller JN. 2008. Polysaccharides: occurrence, significance, and properties. In Glycoscience, eds Fraser-Reid BO, Tatsuta K, Thiem J. Berlin, Heidelberg: Springer. pp. 1413–1435 doi: 10.1007/978-3-540-30429-6_34
[3]

Zhou Y, Petrova SP, Edgar KJ. 2021. Chemical synthesis of polysaccharide–protein and polysaccharide–peptide conjugates: a review. Carbohydrate Polymers 274:118662

doi: 10.1016/j.carbpol.2021.118662
[4]

Liu P, Fei L, Wu D, Zhang Z, Chen W, et al. 2024. Progress in the metabolic kinetics and health benefits of functional polysaccharides from plants, animals and microbes: a review. Carbohydrate Polymer Technologies and Applications 7:100526

doi: 10.1016/j.carpta.2024.100526
[5]

Ali SS, Alsharbaty MHM, Al-Tohamy R, Naji GA, Elsamahy T, et al. 2024. A review of the fungal polysaccharides as natural biopolymers: current applications and future perspective. International Journal of Biological Macromolecules 273:132986

doi: 10.1016/j.ijbiomac.2024.132986
[6]

Muhamad II, Lazim NAM, Selvakumaran S. 2019. Natural polysaccharide-based composites for drug delivery and biomedical applications. In Natural Polysaccharides in Drug Delivery and Biomedical Applications, eds Hasnain MS, Nayak AK. US: Academic Press. pp. 419−440 doi: 10.1016/B978-0-12-817055-7.00018-2
[7]

Xu S, Zhang H, Feng X, Li B, Li T, et al. 2025. Molecularly tailored strategies based on natural polysaccharide structural and functional benefits for precise nutrients delivery. International Journal of Biological Macromolecules 329:147732

doi: 10.1016/j.ijbiomac.2025.147732
[8]

Kapoor DU, Pareek A, Sharma M, Prajapati BG, Suttiruengwong S, et al. 2025. Exploring starch-based excipients in pharmaceutical formulations: versatile applications and future perspectives. European Journal of Pharmaceutics and Biopharmaceutics 212:114727

doi: 10.1016/j.ejpb.2025.114727
[9]

Khaleel Basha S, Syed Muzammil M, Dhandayuthabani R, Sugantha Kumari V. 2021. Polysaccharides as excipient in drug delivery system. Materials Today: Proceedings 36:280−289

doi: 10.1016/j.matpr.2020.04.046
[10]

Mizrahy S, Peer D. 2012. Polysaccharides as building blocks for nanotherapeutics. Chemical Society Reviews 41:2623−2640

doi: 10.1039/C1CS15239D
[11]

Jia X, Fan D, Yang Z, Chang J, Wang Q, et al. 2025. Research progress of natural polysaccharide-based hydrogels in skin tissue regeneration. Gels 12:21

doi: 10.3390/gels12010021
[12]

Zanchetta C, Scandolera A, Reynaud R. 2025. Hyaluronic acid in topical applications: the various forms and biological effects of a hero molecule in the cosmetics industry. Biomolecules 15:1656

doi: 10.3390/biom15121656
[13]

Sharma S, Dwivedi SP, Pattanaik A, Kalyani T. 2025. Synergy-driven polysaccharide-based biomaterials: mechanisms of self-healing, cross-linking, and blending for controlled biotherapeutic delivery and enhanced biomedical applications. Polymer Bulletin 82:12319−12416

doi: 10.1007/s00289-025-06005-w
[14]

Chen SK, Li YH, Wang X, Guo YQ, Song XX, et al. 2023. Evaluation of the "Relative Ordered Structure of Hericium erinaceus Polysaccharide" from different origins: based on similarity and dissimilarity. Journal of Agricultural and Food Chemistry 71:17886−17898

doi: 10.1021/acs.jafc.3c04329
[15]

Harding SE. 1995. Some recent developments in the size and shape analysis of industrial polysaccharides in solution using sedimentation analysis in the analytical ultracentrifuge. Carbohydrate Polymers 28:227−237

doi: 10.1016/0144-8617(95)00146-8
[16]

Frank M. 2015. Conformational analysis of oligosaccharides and polysaccharides using molecular dynamics simulations. Methods in Molecular Biology 1273:359−377

doi: 10.1007/978-1-4939-2343-4_22
[17]

Yapo BM. 2011. Rhamnogalacturonan-I: a structurally puzzling and functionally versatile polysaccharide from plant cell walls and mucilages. Polymer Reviews 51:391−413

doi: 10.1080/15583724.2011.615962
[18]

Li ZW, Du ZM, Wang YW, Feng YX, Zhang R, et al. 2022. Chemical modification, characterization, and activity changes of land plant polysaccharides: a review. Polymers 14:4161

doi: 10.3390/polym14194161
[19]

Dorst KM, Widmalm G. 2024. Conformational preferences at the glycosidic linkage of saccharides in solution as deduced from NMR experiments and MD simulations: comparison to crystal structures. Chemistry 30:e202304047

doi: 10.1002/chem.202304047
[20]

Kato K, Watanabe T, Yamaguchi T. 2025. Deciphering glycan dynamics through nonlinear correlation analysis. Chemical & Pharmaceutical Bulletin 73:639−644

doi: 10.1248/cpb.c25-00243
[21]

Dwivedi R, Maurya AK, Ahmed H, Farrag M, Pomin VH. 2024. Nuclear magnetic resonance-based structural elucidation of novel marine glycans and derived oligosaccharides. Magnetic Resonance in Chemistry 62:269−285

doi: 10.1002/mrc.5377
[22]

Bush CA, Martin-Pastor M, Imberty A. 1999. Structure and conformation of complex carbohydrates of glycoproteins, glycolipids, and bacterial polysaccharides. Annual Review of Biophysics and Biomolecular Structure 28:269−293

doi: 10.1146/annurev.biophys.28.1.269
[23]

Kang X, Kirui A, Dickwella Widanage MC, Mentink-Vigier F, Cosgrove DJ, et al. 2019. Lignin-polysaccharide interactions in plant secondary cell walls revealed by solid-state NMR. Nature Communications 10:347

doi: 10.1038/s41467-018-08252-0
[24]

Guchhait B, Kumari M, Kaity S, Roy S. 2025. Comparison of natural polysaccharides and synthetic polymers in facilitating macromolecular crowding to create customized extracellular matrix-rich supramolecular assemblies from fibroblasts. ACS Applied Bio Materials 8:6701−6721

doi: 10.1021/acsabm.5c01003
[25]

Ankur A, Yarava JR, Gautam I, Scott FJ, Mentink-Vigier F, et al. 2025. Polymorphic α-glucans as structural scaffolds in cryptococcus cell walls for chitin, capsule, and melanin: insights from 13C and 1H solid-state NMR. Angewandte Chemie 64:e202510409

doi: 10.1002/anie.202510409
[26]

Viñarta SC, Delgado OD, Figueroa LIC, Fariña JI. 2013. Effects of thermal, alkaline and ultrasonic treatments on scleroglucan stability and flow behavior. Carbohydrate Polymers 94:496−504

doi: 10.1016/j.carbpol.2013.01.063
[27]

Tanaka T. 2026. Recent advances in chemoenzymatic synthesis of oligosaccharides and polysaccharides. Polymer Chemistry 17:125−147

doi: 10.1039/D5PY00938C
[28]

Wang B, Yan L, Guo S, Wen L, Yu M, et al. 2022. Structural elucidation, modification, and structure-activity relationship of polysaccharides in Chinese herbs: a review. Frontiers in Nutrition 9:908175

doi: 10.3389/fnut.2022.908175
[29]

Chen J, Bi J, Zhou M. 2026. Optimizing ultrasonic-assisted extraction of peach gum polysaccharide: balancing yield and bioactivity through response surface methodology and structural insights. Carbohydrate Polymers 372:124567

doi: 10.1016/j.carbpol.2025.124567
[30]

Lundborg M, Fontana C, Widmalm G. 2011. Automatic structure determination of regular polysaccharides based solely on NMR spectroscopy. Biomacromolecules 12:3851−3855

doi: 10.1021/bm201169y
[31]

Egorova KS, Toukach PV. 2018. Glycoinformatics: bridging isolated islands in the sea of data. Angewandte Chemie 57:14986−14990

doi: 10.1002/anie.201803576
[32]

Lütteke T. 2015. Handling and conversion of carbohydrate sequence formats and monosaccharide notation. Methods in Molecular Biology. 1273:43−54

doi: 10.1007/978-1-4939-2343-4_4
[33]

Ye Z, Xu Q, Jiang L, Cui Q, Liu J, et al. 2025. Bibliometric analysis of artificial intelligence in research of active polysaccharides from traditional Chinese medicine. Chinese Traditional and Herbal Drugs 56:6301−6316
[34]

Mjolsness E, DeCoste D. 2001. Machine learning for science: state of the art and future prospects. Science 293:2051−2055

doi: 10.1126/science.293.5537.2051
[35]

Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science 349:255−260

doi: 10.1126/science.aaa8415
[36]

Green NM, Flanagan MT. 1976. The prediction of the conformation of membrane proteins from the sequence of amino acids. The Biochemical Journal 153:729−732

doi: 10.1042/bj1530729
[37]

Lim VI. 1974. Algorithms for prediction of alpha-helical and beta-structural regions in globular proteins. Journal of Molecular Biology 88:873−894

doi: 10.1016/0022-2836(74)90405-7
[38]

Abarbanel RM, Wieneke PR, Mansfield E, Jaffe DA, Brutlag DL. 1984. Rapid searches for complex patterns in biological molecules. Nucleic Acids Research 12:263−280

doi: 10.1093/nar/12.1part1.263
[39]

Carhart RE, Cash HD, Moore JF. 1988. StrateGene™: object-oriented programming in molecular biology. Bioinformatics 4:3−9

doi: 10.1093/bioinformatics/4.1.3-a
[40]

O'Leary K. 2024. AlphaFold gets an upgrade (and a Nobel). Nature Medicine 30:3393

doi: 10.1038/s41591-024-03392-x
[41]

Huang C, Kannan N, Moremen KW. 2025. Modeling glycans with AlphaFold 3: capabilities, caveats, and limitations. Glycobiology 35:cwaf048

doi: 10.1093/glycob/cwaf048
[42]

Bai C, Chen R, Chen Y, Bai H, Sun H, et al. 2024. Plant polysaccharides extracted by high pressure: a review on yields, physicochemical, structure properties, and bioactivities. International Journal of Biological Macromolecules 263:129939

doi: 10.1016/j.ijbiomac.2024.129939
[43]

Md Yusoff MH, Shafie MH. 2025. Pioneering polysaccharide extraction with deep eutectic solvents: a review on impacts to extraction yield, physicochemical properties and bioactivities. International Journal of Biological Macromolecules 306:141469

doi: 10.1016/j.ijbiomac.2025.141469
[44]

Sun Y, Jia X, Yang R, Qin X, Zhou X, et al. 2022. Deep eutectic solvents boosting solubilization and Se-functionalization of heteropolysaccharide: multiple hydrogen bonds modulation. Carbohydrate Polymers 284:119159

doi: 10.1016/j.carbpol.2022.119159
[45]

Ma L, Guo W, Chen L, Shi J, Wang H, et al. 2025. Deep eutectic solvent extraction of polysaccharides from Lycium barbarum residue: optimization, composition analysis, and antioxidant activity. Journal of Agriculture and Food Research 23:102300

doi: 10.1016/j.jafr.2025.102300
[46]

Ali A, Li R, Zhu R, Mahmood S, Chen Q, et al. 2026. Deep eutectic solvents for green extraction and separation of bioactive compounds from traditional Chinese medicines. Chinese Medicine 21:38

doi: 10.1186/s13020-026-01325-z
[47]

Ibrahim A, Tshibangu MM, Coquelet C, Espitalier F. 2025. Ternary choline chloride-based deep eutectic solvents: a review. ChemEngineering 9:84

doi: 10.3390/chemengineering9040084
[48]

Liu D, Tang W, Zhang G, Chen X, Wu Y, et al. 2025. Morinda citrifolia (noni) polysaccharides: extraction methods, molecular structures, and biological activities. International Journal of Biological Macromolecules 327:147315

doi: 10.1016/j.ijbiomac.2025.147315
[49]

Klamt A. 1995. Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. The Journal of Physical Chemistry 99:2224−2235

doi: 10.1021/j100007a062
[50]

Klamt A, Jonas V, Bürger T, Lohrenz JCW. 1998. Refinement and parametrization of COSMO-RS. The Journal of Physical Chemistry A 102:5074−5085

doi: 10.1021/jp980017s
[51]

Wang D, Zhang M, Law CL, Zhang L. 2024. Natural deep eutectic solvents for the extraction of lentinan from shiitake mushroom: COSMO-RS screening and ANN-GA optimizing conditions. Food Chemistry 430:136990

doi: 10.1016/j.foodchem.2023.136990
[52]

Deng G, Li P, Liang H, Chen T, Zhou L, et al. 2025. Extraction of polysaccharides from Camellia oleifera leaves by dual enzymes combined with deep eutectic solvents screened by ANN and COSMO-RS. International Journal of Biological Macromolecules 305:141131

doi: 10.1016/j.ijbiomac.2025.141131
[53]

Uto T. 2025. Atomistic simulations of polysaccharide materials for insights into their crystal structure, nanostructure, and dissolution mechanism. Polymer Journal 57:33−41

doi: 10.1038/s41428-024-00966-x
[54]

Zhang Y, Chen B, Zhang H, Zhang J, Xue J. 2024. Extraction, purification, structural characterization, bioactivities, modifications and structure-activity relationship of polysaccharides from Ophiopogon japonicus: a review. Frontiers in Nutrition 11:1484865

doi: 10.3389/fnut.2024.1484865
[55]

Garcia-Vaquero M, Rajauria G, Tiwari B, Sweeney T, O'Doherty J. 2018. Extraction and yield optimisation of fucose, glucans and associated antioxidant activities from Laminaria digitata by applying response surface methodology to high intensity ultrasound-assisted extraction. Marine Drugs 16:257

doi: 10.3390/md16080257
[56]

Kouamé KJEP, Falade EO, Zhu Y, Zheng Y, Ye X. 2025. Advances in innovative extraction techniques for polysaccharides, peptides, and polyphenols from distillery by-products: common extraction techniques, emerging technologies, and AI-driven optimization. Food Chemistry 476:143326

doi: 10.1016/j.foodchem.2025.143326
[57]

Wu M, Xu G, Chen Y, Xu X, Zhu S, et al. 2025. Artificial neural network model-based optimization of Polygonum perfoliatum L. Polysaccharide ultrasonic-assisted extraction, structural characterization, and anti-inflammatory activity. Ultrasonics Sonochemistry 123:107654

doi: 10.1016/j.ultsonch.2025.107654
[58]

Dos Santos MCM, Fogarin HM, Murillo-Franco SL, De Souza JP, Filletti ER, et al. 2025. Artificial neural networks to predict the behavior of sugars obtained by acid hydrolysis process from spent coffee grounds. Waste and Biomass Valorization 16:3895−3908

doi: 10.1007/s12649-025-03109-2
[59]

Ma J, Yao J, Ren X, Dong Y, Song R, et al. 2023. Machine learning-assisted data-driven optimization and understanding of the multiple stage process for extraction of polysaccharides and secondary metabolites from natural products. Green Chemistry 25:3057−3068

doi: 10.1039/D2GC04574E
[60]

Duan Y, Wang S, Zhang X, Zhang H, Wang H, et al. 2025. Mechanistic insight into the frequency-dependent ultrasound-assisted extraction of Rosa laevigata Polysaccharides: structure, antioxidant activity, and process optimization. Ultrasonics Sonochemistry 121:107577

doi: 10.1016/j.ultsonch.2025.107577
[61]

Tian Y, Yang X, Chen N, Li C, Yang W. 2024. Data-driven interpretable analysis for polysaccharide yield prediction. Environmental Science and Ecotechnology 19:100321

doi: 10.1016/j.ese.2023.100321
[62]

Guan T, Gong J, Lin J, Palanisamy CP, Pei J, et al. 2025. Machine learning-driven multimodal optimization of selenium biotransformation and flavor profiling in fermented apple–Yacon functional beverages. Innovative Food Science & Emerging Technologies 105:104198

doi: 10.1016/j.ifset.2025.104198
[63]

Chen Q, Zhang W, Wang Y, Cai W, Ni Q, et al. 2025. Genetic algorithm-back propagation neural network model- and response surface methodology-based optimization of polysaccharide extraction from Cinnamomum cassia Presl, isolation, purification and bioactivities. Foods 14:686

doi: 10.3390/foods14040686
[64]

Liao H, Hu S, Yang H, Wang L, Tanaka S, et al. 2025. Data-driven de novo design of super-adhesive hydrogels. Nature 644:89−95

doi: 10.1038/s41586-025-09269-4
[65]

Liu Y, Gao L, Yu Z. 2025. Revealing the complexity of polysaccharides: advances in NMR spectroscopy for structural elucidation and functional characterization. Applied Sciences 15:5246

doi: 10.3390/app15105246
[66]

Ma Y, Zhang L, Ma X, Bai K, Tian Z, et al. 2024. Saccharide mapping as an extraordinary method on characterization and identification of plant and fungi polysaccharides: a review. International Journal of Biological Macromolecules 275:133350

doi: 10.1016/j.ijbiomac.2024.133350
[67]

Liu X, Renard CMGC, Bureau S, Le Bourvellec C. 2021. Revisiting the contribution of ATR-FTIR spectroscopy to characterize plant cell wall polysaccharides. Carbohydrate Polymers 262:117935

doi: 10.1016/j.carbpol.2021.117935
[68]

Bikmurzin R, Bandzevičiūtė R, Maršalka A, Maneikis A, Kalėdienė L. 2022. FT-IR method limitations for β-glucan analysis. Molecules 27:4616

doi: 10.3390/molecules27144616
[69]

Li X, Liu J, Xu L, Sun Y. 2025. Information density-based adaptive band selection for wide dynamic range gas quantification in infrared spectroscopy. Optics Express 33:39855−39874

doi: 10.1364/OE.572011
[70]

Li D, Wang Y, Hu F, Yan C. 2020. Study on two speices of Russula by infrared spectroscopy combined with curve-fitting analysis. Journal of Qujing Normal University 39:41−46

doi: 10.3969/j.issn.1009-8879.2020.03.010
[71]

Zhang J, Zhang J, Zhong Z. 2024. ATR-FTIR and NIR spectra combined with chemometrics and convolutional neural networks for identification of polygonati rhizome. Journal of Molecular Structure 1318:139449

doi: 10.1016/j.molstruc.2024.139449
[72]

Widmalm G. 2013. A perspective on the primary and three-dimensional structures of carbohydrates. Carbohydrate Research 378:123−132

doi: 10.1016/j.carres.2013.02.005
[73]

Agrawal PK. 1992. NMR spectroscopy in the structural elucidation of oligosaccharides and glycosides. Phytochemistry 31:3307−3330

doi: 10.1016/0031-9422(92)83678-r
[74]

Ndukwe IE, Black I, Castro CA, Vlach J, Heiss C, et al. 2023. Permethylation as a strategy for high molecular weight polysaccharide structure analysis by NMR - case study of Xylella fastidiosa EPS. bioRxiv 2023.04.24.538115

doi: 10.1101/2023.04.24.538115
[75]

Smith MJ, Gates EL, Widmalm G, Adams RW, Morris GA, et al. 2023. Resolving the complexity in human milk oligosaccharides using pure shift NMR methods and CASPER. Organic & Biomolecular Chemistry 21:3984−3990

doi: 10.1039/d3ob00421j
[76]

Dorst KM, Widmalm G. 2023. NMR chemical shift prediction and structural elucidation of linker-containing oligo- and polysaccharides using the computer program CASPER. Carbohydrate Research 533:108937

doi: 10.1016/j.carres.2023.108937
[77]

Harvey DJ. 2005. Fragmentation of negative ions from carbohydrates: part 1. Use of nitrate and other anionic adducts for the production of negative ion electrospray spectra from N-linked carbohydrates. Journal of the American Society for Mass Spectrometry 16:622−630

doi: 10.1016/j.jasms.2005.01.004
[78]

Zhu Y, Dong Y, Li D, Liu B, Ye Q, et al. 2026. How to identify target glycosidic linkages by UPLC-MS/MS? Case study of oligosaccharides and polysaccharides composed of glucose. Food Hydrocolloids 172:111914

doi: 10.1016/j.foodhyd.2025.111914
[79]

Ashline D, Singh S, Hanneman A, Reinhold V. 2005. Congruent strategies for carbohydrate sequencing. 1. Mining structural details by MSn. Analytical Chemistry 77:6250−6262

doi: 10.1021/ac050724z
[80]

An HJ, Lebrilla CB. 2011. Structure elucidation of native N- and O-linked glycans by tandem mass spectrometry (tutorial). Mass Spectrometry Reviews 30:560−578

doi: 10.1002/mas.20283
[81]

Ropartz D, Giuliani A, Fanuel M, Hervé C, Czjzek M, et al. 2016. Online coupling of high-resolution chromatography with extreme UV photon activation tandem mass spectrometry: application to the structural investigation of complex glycans by dissociative photoionization. Analytica Chimica Acta 933:1−9

doi: 10.1016/j.aca.2016.05.036
[82]

Geue N, Safferthal M, Pagel K. 2025. Collision-induced fragmentation of oligosaccharides: mechanistic insights for mass spectrometry-based glycomics. Angewandte Chemie 64:e202511591

doi: 10.1002/anie.202511591
[83]

Urban J, Jin C, Thomsson KA, Karlsson NG, Ives CM, et al. 2024. Predicting glycan structure from tandem mass spectrometry via deep learning. Nature Methods 21:1206−1215

doi: 10.1038/s41592-024-02314-6
[84]

Abtheen EA, Singh A, Sriram S, Chen C, Neelamegham S, et al. 2025. Transformer-based deep learning for glycan structure inference from tandem mass spectrometry. bioRxiv 2025.07.02.662857.

doi: 10.1101/2025.07.02.662857
[85]

Akutsu T. 2010. Tree edit distance problems: algorithms and applications to bioinformatics. IEICE Transactions on Information E93-D:208−218

doi: 10.1587/transinf.E93.D.208
[86]

Yang M, Ren W, Li G, Yang P, Chen R, et al. 2022. The effect of structure and preparation method on the bioactivity of polysaccharides from plants and fungi. Food & Function 13:12541−12560

doi: 10.1039/d2fo02029g
[87]

Tucker MR, Lou H, Aubert MK, Wilkinson LG, Little A, et al. 2018. Exploring the role of cell wall-related genes and polysaccharides during plant development. Plants 7:42

doi: 10.3390/plants7020042
[88]

Naran R, Chen G, Carpita NC. 2008. Novel rhamnogalacturonan I and arabinoxylan polysaccharides of flax seed mucilage. Plant Physiology 148:132−141

doi: 10.1104/pp.108.123513
[89]

Darvill AG, McNeil M, Albersheim P. 1978. Structure of plant cell walls: VIII. a new pectic polysaccharide. Plant Physiology 62:418−422

doi: 10.1104/pp.62.3.418
[90]

Hellmann MJ, Gillet D, Trombotto S, Raetz S, Moerschbacher BM, et al. 2024. Heterogeneously deacetylated chitosans possess an unexpected regular pattern favoring acetylation at every third position. Nature Communications 15:6695

doi: 10.1038/s41467-024-50857-1
[91]

Byankina Barabanova AO, Sokolova EV, Anastyuk SD, Isakov VV, Glazunov VP, et al. 2013. Polysaccharide structure of tetrasporic red seaweed Tichocarpus crinitus. Carbohydrate Polymers 98:26−35

doi: 10.1016/j.carbpol.2013.04.063
[92]

Wu Y, Zhou H, Wei K, Zhang T, Che Y, et al. 2022. Structure of a new glycyrrhiza polysaccharide and its immunomodulatory activity. Frontiers in Immunology 13:1007186

doi: 10.3389/fimmu.2022.1007186
[93]

Wu Q, Luo M, Yao X, Yu L. 2020. Purification, structural characterization, and antioxidant activity of the COP-W1 polysaccharide from Codonopsis tangshen Oliv. Carbohydrate Polymers 236:116020

doi: 10.1016/j.carbpol.2020.116020
[94]

Wang A, Zhang H, Yang M, Wang X, Wang F, et al. 2025. Structural characterization and bioactivity analysis of ultrasound-assisted polysaccharides from Codonopsis pilosula: insights into antioxidant and hypoglycemic potentials. Chemistry & Biodiversity 22:e202500565

doi: 10.1002/cbdv.202500565
[95]

Sissi C, Naggi A, Torri G, Palumbo M. 2003. Modulation of antithrombin-protease interactions by semisynthetic low-molecular-weight heparins with different sulfation patterns. Seminars in Thrombosis and Hemostasis 29:661−670

doi: 10.1055/s-2004-815633
[96]

Naggi A, De Cristofano B, Bisio A, Torri G, Casu B. 2001. Generation of anti-factor Xa active, 3-O-sulfated glucosamine-rich sequences by controlled desulfation of oversulfated heparins. Carbohydrate Research 336:283−290

doi: 10.1016/s0008-6215(01)00270-1
[97]

Bharadwaj VS, Westawker LP, Crowley MF. 2021. Towards elucidating structure-spectra relationships in rhamnogalacturonan II: computational protocols for accurate 13C and 1H shifts for apiose and its borate esters. Frontiers in Molecular Biosciences 8:756219

doi: 10.3389/fmolb.2021.756219
[98]

Zhao W, Deligey F, Chandra Shekar S, Mentink-Vigier F, Wang T. 2022. Current limitations of solid-state NMR in carbohydrate and cell wall research. Journal of Magnetic Resonance 341:107263

doi: 10.1016/j.jmr.2022.107263
[99]

Ndukwe IE, Black I, Heiss C, Azadi P. 2020. Evaluating the utility of permethylated polysaccharide solution NMR data for characterization of insoluble plant cell wall polysaccharides. Analytical Chemistry 92:13221−13228

doi: 10.1021/acs.analchem.0c02379
[100]

Dialpuri JS, Bagdonas H, Schofield LC, Pham PT, Holland L, et al. 2024. Monitoring carbohydrate 3D structure quality with the Privateer database. Beilstein Journal of Organic Chemistry 20:931−939

doi: 10.3762/bjoc.20.83
[101]

Altmann F, Helm J, Pabst M, Stadlmann J. 2024. Introduction of a human- and keyboard-friendly N-glycan nomenclature. Beilstein Journal of Organic Chemistry 20:607−620

doi: 10.3762/bjoc.20.53
[102]

McNaught AD. 1997. Nomenclature of carbohydrates (recommendations 1996). Advances in Carbohydrate Chemistry and Biochemistry 52:43−177

doi: 10.1016/S0065-2318(08)60090-6
[103]

Bohne-Lang A, Lang E, Förster T, von der Lieth CW. 2001. LINUCS: linear notation for unique description of carbohydrate sequences. Carbohydrate Research 336:1−11

doi: 10.1016/s0008-6215(01)00230-0
[104]

Joeres R, Bojar D, Kalinina OV. 2023. GlyLES: grammar-based parsing of glycans from IUPAC-condensed to SMILES. Journal of Cheminformatics 15:37

doi: 10.1186/s13321-023-00704-0
[105]

Herget S, Ranzinger R, Maass K, von der Lieth CW. 2008. GlycoCT-a unifying sequence format for carbohydrates. Carbohydrate research 343:2162−2171

doi: 10.1016/j.carres.2008.03.011
[106]

Campbell MP, Peterson R, Mariethoz J, Gasteiger E, Akune Y, et al. 2014. UniCarbKB: building a knowledge platform for glycoproteomics. Nucleic Acids Research 42:D215−D221

doi: 10.1093/nar/gkt1128
[107]

Ranzinger R, Herget S, Wetter T, von der Lieth CW. 2008. GlycomeDB - integration of open-access carbohydrate structure databases. BMC Bioinformatics 9:384

doi: 10.1186/1471-2105-9-384
[108]

Clerc O, Mariethoz J, Rivet A, Lisacek F, Pérez S, et al. 2019. A pipeline to translate glycosaminoglycan sequences into 3D models. Application to the exploration of glycosaminoglycan conformational space. Glycobiology 29:36−44

doi: 10.1093/glycob/cwy084
[109]

Tanaka K, Aoki-Kinoshita KF, Kotera M, Sawaki H, Tsuchiya S, et al. 2014. WURCS: the Web3 unique representation of carbohydrate structures. Journal of Chemical Information and Modeling 54:1558−1566

doi: 10.1021/ci400571e
[110]

Aoki KF, Yamaguchi A, Ueda N, Akutsu T, Mamitsuka H, et al. 2004. KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains. Nucleic Acids Research 32:W267−W272

doi: 10.1093/nar/gkh473
[111]

Carpenter EJ, Seth S, Yue N, Greiner R, Derda R. 2022. GlyNet: a multi-task neural network for predicting protein-glycan interactions. Chemical Science 13:6669−6686

doi: 10.1039/d1sc05681f
[112]

Cao Y, Park S-J, Mehta AY, Cummings RD, Im W. 2020. GlyMDB: Glycan Microarray Database and analysis toolset. Bioinformatics 36:2438−2442

doi: 10.1093/bioinformatics/btz934
[113]

Jiang H, Aoki-Kinoshita KF, Ching WK. 2011. Extracting glycan motifs using a biochemicallyweighted kernel. Bioinformation 7:405−412

doi: 10.6026/97320630007405
[114]

Bojar D, Camacho DM, Collins JJ. 2020. Using natural language processing to learn the grammar of glycans. bioRxiv:preprint

doi: 10.1101/2020.01.10.902114
[115]

Burkholz R, Quackenbush J, Bojar D. 2021. Using graph convolutional neural networks to learn a representation for glycans. Cell Reports 35:109251

doi: 10.1016/j.celrep.2021.109251
[116]

Mohapatra S, An J, Gómez-Bombarelli R. 2021. Chemistry-informed macromolecule graph representation for similarity computation and supervised learning. arXiv:2103.02565

doi: 10.48550/arXiv.2103.02565
[117]

Xu M, Song J, Wu K, Zhou X, Cui B, et al. 2025. Modeling all-atom glycan structures via hierarchical message passing and multi-scale pre-training. arXiv:2506.01376

doi: 10.48550/arXiv.2506.01376
[118]

Kitani A, Zhang B, Himori K, Matsui Y. 2025. GlycanGT: a foundation model for glycan graphs with pretrained representation and generative learning. bioRxiv:preprint

doi: 10.64898/2025.12.14.694171
[119]

Mao S, Zhang X, Yang M, Duan JA, Xiao P. 2025. A comprehensive review on the antitumor mechanisms of polysaccharides and their structure-activity relationships—current insights and future directions. International Journal of Biological Macromolecules 319:145355

doi: 10.1016/j.ijbiomac.2025.145355
[120]

Shi L, He Q, Li J, Liu Y, Cao Y, et al. 2024. Polysaccharides in fruits: biological activities, structures, and structure-activity relationships and influencing factors-a review. Food Chemistry 451:139408

doi: 10.1016/j.foodchem.2024.139408
[121]

Ferreira SS, Passos CP, Madureira P, Vilanova M, Coimbra MA. 2015. Structure–function relationships of immunostimulatory polysaccharides: a review. Carbohydrate Polymers 132:378−396

doi: 10.1016/j.carbpol.2015.05.079
[122]

Chen YZ, Hassoun S. 2025. Learning from all views: a multiview contrastive framework for metabolite annotation. bioRxiv preprint

doi: 10.1101/2025.11.12.688047
[123]

Qin S, Tian Z. 2025. Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model. Analytical and Bioanalytical Chemistry 417:1001−1014

doi: 10.1007/s00216-024-05505-4
[124]

Lu J, Yang Y, Hong EK, Yin X, Wang X, et al. 2024. Analyzing the structure-activity relationship of raspberry polysaccharides using interpretable artificial neural network model. International Journal of Biological Macromolecules 264:130354

doi: 10.1016/j.ijbiomac.2024.130354
[125]

Bibekar P, Krapp L, Peraro MD. 2024. PeSTo-Carbs: geometric deep learning for prediction of protein-carbohydrate binding interfaces. Journal of Chemical Theory and Computation 20:2985−2991

doi: 10.1021/acs.jctc.3c01145
[126]

He X, Zhao L, Tian Y, Li R, Chu Q, et al. 2024. Highly accurate carbohydrate-binding site prediction with DeepGlycanSite. Nature Communications 15:5163

doi: 10.1038/s41467-024-49516-2
[127]

Carpenter EJ, Peng C, Haregu S, Twells N, Woudstra L, et al. 2025. Atom-level machine learning of protein-glycan interactions and cross-chiral recognition in glycobiology. Science Advances 11:eadx6373

doi: 10.1126/sciadv.adx6373
[128]

Wu Q, Chen Y, Zou W, Pan YB, Lin P, et al. 2023. Genome-wide characterization of sugarcane catalase gene family identifies a ScCAT1 gene associated disease resistance. International Journal of Biological Macromolecules 232:123398

doi: 10.1016/j.ijbiomac.2023.123398
[129]

Zhao JY, Guo J, Ye HY, Luo PW, Zhu Q, et al. 2026. Structure-function relationships of edible and medicinal mushroom polysaccharides: structural analysis, target molecules and signaling pathways. Carbohydrate Polymers 371:124463

doi: 10.1016/j.carbpol.2025.124463
[130]

Yang L, Hou H, Yan H, Deng H, Guan B, et al. 2026. Molecular mechanism and structure-activity relationships of natural source polysaccharides in intervening type 2 diabetes mellitus through antioxidant effects: a systematic review. Carbohydrate Polymers 377:124830

doi: 10.1016/j.carbpol.2025.124830
[131]

Ding Z, Zhang N, Ding M, Zhang S, Yao X, et al. 2025. Optimization of ultrasonic extraction processes, structural characteristics and potential antipyretic mechanism of a glucan from Tetrastigma hemsleyanum Diels. Ultrasonics Sonochemistry 120:107456

doi: 10.1016/j.ultsonch.2025.107456
[132]

Bojar D, Powers RK, Camacho DM, Collins JJ. 2021. Deep-learning resources for studying glycan-mediated host-microbe interactions. Cell Host & Microbe 29:132−144.e3

doi: 10.1016/j.chom.2020.10.004
[133]

Rehman A, Jafari SM, Tong Q, Riaz T, Assadpour E, et al. 2020. Drug nanodelivery systems based on natural polysaccharides against different diseases. Advances in Colloid and Interface Science 284:102251

doi: 10.1016/j.cis.2020.102251
[134]

Li Q, Liu X, Yan C, Zhao B, Zhao Y, et al. 2023. Polysaccharide-based stimulus-responsive nanomedicines for combination cancer immunotherapy. Small 19:e2206211

doi: 10.1002/smll.202206211
[135]

Abdalla Y, McCoubrey LE, Ferraro F, Sonnleitner LM, Guinet Y, et al. 2024. Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery. Journal of Controlled Release 374:103−111

doi: 10.1016/j.jconrel.2024.08.010
[136]

Yadav A, Jayaprakash B, Jasim LH, Kundlas M, Anad MY, et al. 2025. Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application. Scientific Reports 15:22461

doi: 10.1038/s41598-025-06227-y
[137]

Köhler T, Kunchapu S, Vollrath A, Rezaei K, Kimmig J, et al. 2026. Predicting acetalated dextran nanoparticle features: controlled synthesis, formulation, and testing in a high-throughput process. Carbohydrate Polymers 380:124890

doi: 10.1016/j.carbpol.2026.124890
[138]

Rong Q, Lei W, Liu M. 2018. Conductive hydrogels as smart materials for flexible electronic devices. Chemistry 24:16930−16943

doi: 10.1002/chem.201801302
[139]

Cao H, Duan L, Zhang Y, Cao J, Zhang K. 2021. Current hydrogel advances in physicochemical and biological response-driven biomedical application diversity. Signal Transduction and Targeted Therapy 6:426

doi: 10.1038/s41392-021-00830-x
[140]

Garcia-Del Rio L, Diaz-Rodriguez P, Landin M. 2021. Design of novel orotransmucosal vaccine-delivery platforms using artificial intelligence. European Journal of Pharmaceutics and Biopharmaceutics 159:36−43

doi: 10.1016/j.ejpb.2020.12.018
[141]

Deng D, Liang L, Su K, Gu H, Wang X, et al. 2025. Smart hydrogel dressing for machine learning-enabled visual monitoring and promote diabetic wound healing. Nano Today 60:102559

doi: 10.1016/j.nantod.2024.102559
[142]

Pérez-Ramos A, Mohedano ML, Pardo MÁ, López P. 2018. β-Glucan-Producing Pediococcus parvulus 2. 6: test of probiotic and immunomodulatory properties in zebrafish models. Frontiers in Microbiology 9:1684

doi: 10.3389/fmicb.2018.01684
[143]

Yue Y, Han J, Shen X, Zhu F, Liu Y, et al. 2024. Structural characteristics, immune-activating mechanisms in vitro, and immunomodulatory effects in vivo of the exopolysaccharide EPS53 from Streptococcus thermophilus XJ53. Carbohydrate Polymers 340:122259

doi: 10.1016/j.carbpol.2024.122259
[144]

Mo X, Shen A, Han Y, Xu L, Miao J, et al. 2025. Polysaccharide nanoadjuvants engineered via phenotype-specific nanoprobe-assisted phenotypic screen reprogram macrophage cell functions for cancer and rheumatoid arthritis therapy. ACS Nano 19:12920−12936

doi: 10.1021/acsnano.4c16671
[145]

Suzuki E, Serata M, Sako T, Sato S, Iino T, et al. 2025. Glycophenotyping of mutants of Lacticaseibacillus paracasei by lectin microarray. Applied and Environmental Microbiology 91:e01707-24

doi: 10.1128/aem.01707-24
[146]

Lundstrøm J, Korhonen E, Lisacek F, Bojar D. 2022. LectinOracle: a generalizable deep learning model for lectin-glycan binding prediction. Advanced Science 9:e2103807

doi: 10.1002/advs.202103807
[147]

Abramson J, Adler J, Dunger J, Evans R, Green T, et al. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493−500

doi: 10.1038/s41586-024-07487-w
[148]

Barnett AJ, Kc R, Pandey P, Somasiri P, Fairfax KA, et al. 2026. Benchmarking generative AI protein models reveals differences between structural and sequence-based approaches. Genomics, Proteomics & Bioinformatics 00:qzag014

doi: 10.1093/gpbjnl/qzag014
[149]

Luthfi M, Simpkin AJ, Elliott LG, Sompornpisut P, Rigden DJ. 2026. Physical implausibility of carbohydrate ligands in results of deep learning-based cofolding methods. Journal of Chemical Information and Modeling 66:3456−3463

doi: 10.1021/acs.jcim.5c03075
[150]

Gainza P, Wehrle S, Van Hall-Beauvais A, Marchand A, Scheck A, et al. 2023. De novo design of protein interactions with learned surface fingerprints. Nature 617:176−184

doi: 10.1038/s41586-023-05993-x
[151]

Kim S, Oh S, Woo H, Sim J, Seok C, et al. 2025. Deep learning molecular interaction motifs from receptor structures alone. Journal of Cheminformatics 17:113

doi: 10.1186/s13321-025-01055-8
[152]

Liu C, Li M, Tan Y, Gou W, Fan G, et al. 2025. Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering. Bioinformatics 41:btaf446

doi: 10.1093/bioinformatics/btaf446