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第46卷第6期                     丁   妍,李晓林,徐华娥. 人工智能在药物递送中的应用[J].
                  2026年6月                    南京医科大学学报(自然科学版),2026,46(6):911-918,934                    ·917  ·


               [5] LIU F,NIU Y,ZHANG Q H,et al. A foundational archi⁃  spectra,compression force and particle size distribution
                    tecture for AI agents in healthcare[J]. Cell Rep Med,  as input data[J]. Int J Pharm,2021,597:120338

                    2025,6(10):102374                            [17]SANKALIA M G,MASHRU R C,SANKALIA J M,et al.
               [6] DAS S,DEY R,NAYAK A K. Artificial intelligence in   Papain entrapment in alginate beads for stability improve⁃
                     pharmacy[J]. Indian J Pharm Educ Res,2021,55(2),  ment and site⁃specific delivery:physicochemical charac⁃
                     304-318                                           terization and factorial optimization using neural network
               [7] FARIZHANDI A A K,ALISHIRI M,LAU R. Machine          modeling[J]. AAPS PharmSciTech,2005,6(2):31
                     learning approach for carrier surface design in carrier ⁃  [18] LABOUTA H I,EL⁃KHORDAGUI L K,MOLOKHIA A
                     based dry powder inhalation[J]. Comput Chem Eng,  M,et al. Multivariate modeling of encapsulation and release
                     2021,151:107367                                   of an ionizable drug from polymer microspheres[J]. J
               [8] CHAUHAN S,O’CALLAGHAN S,WALL A,et al. Using         Pharm Sci,2009,98(12):4603-4615
                    peptidomics and machine learning to assess effects of  [19]张安阳,范田园. 运用人工神经网络和响应曲面法体外
                    drying processes on the peptide profile within a functional  优化阿司匹林海藻酸钙胃漂浮微球[J]. 北京大学学报
                    ingredient[J]. Processes,2021,9(3):425            (医学版),2010,42(2):197-201
               [9] KESKES S,HANINI S,HENTABLI M,et al. Artificial      ZHANG A Y,FAN T Y. Optimization of calcium alginate
                    intelligence and mathematical modelling of the drying  floating microspheres loading aspirin by artificial neural
                    kinetics of pharmaceutical powders[J]. Kem Ind,2020,  networks and response surface methodology[J]. Journal
                    69(3/4):137-152                                    of Peking University(Health Sciences),2010,42(2):
               [10]ZHAO J,TIAN G,QIU Y Y,et al. Rapid quantification of  197-201
                    active pharmaceutical ingredient for sugar ⁃ free Yangwei  [20]MEDAREVIC D P,KLEINEBUDDE P,DJURIŠ J,et al.
                    granules in commercial production using FT ⁃ NIR spec⁃  Combined application of mixture experimental design and
                    troscopy based on machine learning techniques[J]. Spec⁃  artificial neural networks in the solid dispersion develop⁃
                    trochim Acta Part A Mol Biomol Spectrosc,2021,245:  ment[J]. Drug Dev Ind Pharm,2016,42(3):389-402
                    118878                                       [21]BARMPALEXIS P,KOUTSIDIS I,KARAVAS E,et al.
               [11]LANDIN M. Artificial intelligence tools for scaling up of  Development of PVP/PEG mixtures as appropriate carriers
                    high shear wet granulation process[J]. J Pharm Sci,  for the preparation of drug solid dispersions by melt
                    2017,106(1):273-277                                mixing technique and optimization of dissolution using
               [12]MA X Y,KITTIKUNAKORN N,SORMAN B,et al. Appli⁃       artificial neural networks[J]. Eur J Pharm Biopharm,
                    cation of deep learning convolutional neural networks for  2013,85(3):1219-1231
                    internal tablet defect detection:high accuracy,through⁃  [22] GAO H L,WANG W,DONG J,et al. An integrated
                    put,and adaptability[J]. J Pharm Sci,2020,109(4):  computational methodology with data ⁃ driven machine
                    1547-1557                                          learning,molecular modeling and PBPK modeling to
               [13]OBEID S,MADŽAREVIC M,KRKOBABIC M,et al. Pre⁃        accelerate solid dispersion formulation design[J]. Eur J
                    dicting drug release from diazepam FDM printed tablets  Pharm Biopharm,2021,158:336-346
                    using deep learning approach:influence of process para⁃  [23]HAN R,XIONG H,YE Z,et al. Predicting physical stability
                    meters and tablet surface/volume ratio[J]. Int J Pharm,  of solid dispersions by machine learning techniques[J]. J
                    2021,601:120507                                    Control Release,2019,311/312:16-25
               [14]WESTPHAL E,SEITZ H. A machine learning method for  [24]MEHTA C H,NARAYAN R,NAYAK U Y. Computational
                    defect detection and visualization in selective laser sinte⁃  modeling for formulation design[J]. Drug Discov Today,
                    ring based on convolutional neural networks[J]. Addit  2019,24(3):781-788
                    Manuf,2021,41:101965                         [25]XU L,WANG H,XU Y,et al. Machine learning⁃assisted
               [15] PETROVIC J,IBRIC S,BETZ G,et al. Optimization of   sensor array based on poly(amidoamine)(PAMAM)den⁃
                    matrix tablets controlled drug release using Elman dynamic  drimers for diagnosing Alzheimer’s disease[J]. ACS Sens,
                    neural networks and decision trees[J]. Int J Pharm,  2022,7(5):1315-1322
                    2012,428(1/2):57-67                          [26]HO D,WANG P,KEE T. Artificial intelligence in nano⁃
               [16]GALATA D L,KÖNYVES Z,NAGY B,et al. Real⁃time        medicine[J]. Nanoscale Horiz,2019,4(2):365-377
                    release testing of dissolution based on surrogate models  [27]ASADI H,ROSTAMIZADEH K,SALARI D,et al. Prepa⁃
                    developed by machine learning algorithms using NIR  ration of biodegradable nanoparticles of tri ⁃ block PLA ⁃
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