{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:08:40Z","timestamp":1792789720701,"version":"4.5.0"},"reference-count":77,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Higher Education Commission"},{"id":[{"id":"https:\/\/ror.org\/038y3sz68","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Royal Society Short Industrial Fellowship","award":["2020\u201322"],"award-info":[{"award-number":["2020\u201322"]}],"id":[{"id":"https:\/\/ror.org\/03wnrjx87","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Royal Society International Exchanges Programme","award":["IES\\R2\\232014"],"award-info":[{"award-number":["IES\\R2\\232014"]}],"id":[{"id":"https:\/\/ror.org\/03wnrjx87","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Royal Society International Exchanges Programme","award":["2023\u201325"],"award-info":[{"award-number":["2023\u201325"]}],"id":[{"id":"https:\/\/ror.org\/03wnrjx87","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/V028618\/1"],"award-info":[{"award-number":["EP\/V028618\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"award":["EP\/V028618\/1"],"award-info":[{"award-number":["EP\/V028618\/1"]}],"id":[{"id":"https:\/\/ror.org\/0439y7842","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>This review paper offers a comprehensive exploration of the many Artificial Intelligence (AI) applications in monoclonal antibody (mAb) manufacturing, covering a wide spectrum of topics, including upstream and downstream processes, process control, optimisation, product property monitoring and regulatory compliance. We present several case studies showcasing the predictive modelling capabilities of AI and ML algorithms for cell culture optimisation, media formulation, and bioreactor control, highlighting their potential to enhance cell growth, productivity, and product quality. Furthermore, we explore its potential in process monitoring, fault detection, and real-time decision-making, towards improved process robustness and reduced production costs.<\/jats:p>","DOI":"10.3390\/molecules31183313","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:12:09Z","timestamp":1789999929000},"page":"3313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing"],"prefix":"10.3390","volume":"31","author":[{"given":"Fawad","family":"Abidi","sequence":"first","affiliation":[{"name":"Institute for Materials and Processes (IMP), School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2210-6784","authenticated-orcid":false,"given":"Dimitrios I.","family":"Gerogiorgis","sequence":"additional","affiliation":[{"name":"Institute for Materials and Processes (IMP), School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ymben.2020.10.005","article-title":"Machine learning for metabolic engineering: A review","volume":"63","author":"Lawson","year":"2021","journal-title":"Metab. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2570711","DOI":"10.1080\/00219592.2025.2570711","article-title":"Process Analytical Technologies (PAT) for accurate quantification of monoclonal antibodies (mAbs)","volume":"59","author":"Abidi","year":"2026","journal-title":"J. Chem. Eng. Jpn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100080","DOI":"10.1016\/j.dche.2022.100080","article-title":"A scoping review of supervised learning modelling and data-driven optimisation in monoclonal antibody process development","volume":"7","author":"Pham","year":"2023","journal-title":"Digit. Chem. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/B978-0-12-818597-1.50023-0","article-title":"The unreasonable effectiveness of equations: Advanced modeling for biopharmaceutical process development","volume":"47","author":"Rolandi","year":"2019","journal-title":"Comput. Aided Chem. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dewasme, L., Cote, F., Filee, P., Hansson, A., and Wouwer, A.V. (2017). Macroscopic dynamic modeling of sequential batch cultures of hybridoma cells: An experimental validation. Bioengineering, 4.","DOI":"10.3390\/bioengineering4010017"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108164","DOI":"10.1016\/j.compchemeng.2023.108164","article-title":"Practical data-driven modeling and robust predictive control of mammalian cell fed-batch process","volume":"171","author":"Dewasme","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e00131","DOI":"10.1016\/j.mec.2020.e00131","article-title":"Harnessing the potential of artificial neural networks for predicting protein glycosylation","volume":"10","author":"Kotidis","year":"2020","journal-title":"Metab. Eng. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1002\/bit.26960","article-title":"Model-based optimization of antibody galactosylation in CHO cell culture","volume":"116","author":"Kotidis","year":"2019","journal-title":"Biotechnol. Bioeng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1002\/bit.27120","article-title":"Hybrid physics-based and data-driven modeling for bioprocess online simulation & optimization","volume":"116","author":"Zhang","year":"2019","journal-title":"Biotechnol. Bioeng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.tibtech.2022.08.007","article-title":"Artificial intelligence and machine learning applications in biopharmaceutical manufacturing","volume":"41","author":"Rathore","year":"2023","journal-title":"Trends Biotechnol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pinto, J., Ramos, J.R.C., Costa, R.S., Rossell, S., Dumas, P., and Oliveira, R. (2023). Hybrid deep modelling of a CHO-K1 fed-batch process: Combining first-principles with deep neural networks. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1237963"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1002\/bit.29024","article-title":"Modeling and optimization of recombinant Tocilizumab production from Pichia pastoris using response surface methodology and artificial neural network","volume":"122","author":"Das","year":"2024","journal-title":"Biotechnol. Bioeng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/0098-1354(95)00190-5","article-title":"A hybrid neural network\u2014First principles approach for modelling of cell metabolism","volume":"20","author":"Fu","year":"1996","journal-title":"Comput. Chem. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Quantrille, T.\u0395., and Liu, Y.A. (1991). Artificial Intelligence in Chemical Engineering, Academic Press.","DOI":"10.1016\/B978-0-08-057121-8.50017-8"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107471","DOI":"10.1016\/j.compchemeng.2021.107471","article-title":"Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells","volume":"154","author":"Antonakoudis","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/s41467-019-13867-y","article-title":"Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion","volume":"11","author":"Gutierrez","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Botton, A., Barberi, G., and Facco, P. (2022). Data augmentation to support biopharmaceutical process development through digital models\u2014A proof of concept. Processes, 10.","DOI":"10.3390\/pr10091796"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.arcontrol.2018.09.005","article-title":"Reinforcement learning for control: Performance, stability, and deep approximators","volume":"46","author":"Kober","year":"2018","journal-title":"Annu. Rev. Control"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e17306","DOI":"10.1002\/aic.17306","article-title":"Using process data to generate an optimal control policy via apprenticeship and reinforcement learning","volume":"67","author":"Mowbray","year":"2021","journal-title":"AIChE J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.compchemeng.2004.11.004","article-title":"On-line adaptation of neural networks for bioprocess control","volume":"29","author":"Gadkar","year":"2005","journal-title":"Comput. Chem. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1016\/j.jprocont.2013.08.008","article-title":"Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty","volume":"23","author":"Lucia","year":"2013","journal-title":"J. Process Control"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108100","DOI":"10.1016\/j.compchemeng.2022.108100","article-title":"Pharmaceutical process optimisation: Decision support under high uncertainty","volume":"170","author":"Manapragada","year":"2023","journal-title":"Comput. Chem. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s41540-023-00284-7","article-title":"Employing active learning in the optimization of culture medium for mammalian cells","volume":"9","author":"Hashizume","year":"2023","journal-title":"npj Syst. Biol. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Baako, T.-M.D., Kulkarni, S.K., McClendon, J.L., Harcum, S.W., and Gilmore, J. (2024). Machine learning and deep learning strategies for Chinese hamster ovary cell bioprocess optimization. Fermentation, 10.","DOI":"10.3390\/fermentation10050234"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1002\/cjce.25446","article-title":"Physics-informed neural networks guided modelling and multi-objective optimization of a mAb production process","volume":"103","author":"Alam","year":"2025","journal-title":"Can. J. Chem. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"177","DOI":"10.2147\/BTT.S515078","article-title":"Advancing monoclonal antibody manufacturing: Process optimization, cost reduction strategies, and emerging technologies","volume":"19","author":"Ranbhor","year":"2025","journal-title":"Biol. Targets Ther."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107791","DOI":"10.1016\/j.bej.2020.107791","article-title":"Using data analytics to accelerate biopharmaceutical process scale-up","volume":"164","author":"Facco","year":"2020","journal-title":"Biochem. Eng. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2400023","DOI":"10.1002\/elsc.202400023","article-title":"A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments","volume":"24","author":"Alavijeh","year":"2024","journal-title":"Eng. Life Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.talanta.2018.02.109","article-title":"Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach","volume":"184","author":"Le","year":"2018","journal-title":"Talanta"},{"key":"ref_30","first-page":"2825","article-title":"Duchesnay, Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","article-title":"Nonlinear component analysis as a kernel eigenvalue problem","volume":"10","author":"Smola","year":"1998","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s11095-024-03663-9","article-title":"Convolutional neural networks guided Raman spectroscopy as a process analytical technology (PAT) tool for monitoring and simultaneous prediction of monoclonal antibody charge variants","volume":"41","author":"Nitika","year":"2024","journal-title":"Pharm. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3923\/ajsr.2008.525.531","article-title":"Monoclonal antibody production: Media optimization for enhancement the cell viability of hybridoma cell","volume":"1","author":"Mel","year":"2008","journal-title":"Asian J. Sci. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ibrahim, S., and Abdul Wahab, N. (2022). Improved artificial neural network training based on response surface methodology for membrane flux prediction. Membranes, 12.","DOI":"10.3390\/membranes12080726"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1007\/s10616-019-00330-5","article-title":"Optimization of cultivation conditions for monoclonal IgM antibody production by M1A2 hybridoma using artificial neural network","volume":"71","author":"Bashokouh","year":"2019","journal-title":"Cytotechnology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ejpb.2016.05.009","article-title":"Estimation of design space for an extrusion\u2013spheronization process using response surface methodology and artificial neural network modelling","volume":"106","author":"Kelemen","year":"2016","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jconrel.2006.01.005","article-title":"Injection of PEGylated liposomes in rats elicits PEG specific IgM, which is responsible for rapid elimination of a second dose of PEGylated liposomes","volume":"112","author":"Ishida","year":"2006","journal-title":"J. Control Release"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1089\/hyb.2007.0548","article-title":"Cell growth, cell-cycle progress, and antibody production in hybridoma cells cultivated under mild hypothermic conditions","volume":"27","author":"Chong","year":"2008","journal-title":"Hybridoma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"109357","DOI":"10.1016\/j.bej.2024.109357","article-title":"A deep learning-aided multi-objective optimization of a downstream process for production of monoclonal antibody products","volume":"208","author":"Alam","year":"2024","journal-title":"Biochem. Eng. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1021\/bp070410s","article-title":"Strategic biopharmaceutical portfolio development: An analysis of constraint-induced implications","volume":"24","author":"George","year":"2008","journal-title":"Biotechnol. Prog."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/S1570-7946(09)70177-4","article-title":"Combinatorial optimisation algorithms for strategic biopharmaceutical portfolio & capacity management","volume":"26","author":"George","year":"2009","journal-title":"Comput.-Aided Chem. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/B978-0-444-63234-0.50027-0","article-title":"Mixed integer optimisation of antibody purification processes","volume":"32","author":"Liu","year":"2013","journal-title":"Comput.-Aided Chem. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jones, W., and Gerogiorgis, D.I. (2026). Plantwide optimisation of monoclonal antibody (mab) manufacturing platforms via Mixed Integer Nonlinear Programming (MINLP). R. Soc. Open Sci., in press.","DOI":"10.1098\/rsos.251014"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107896","DOI":"10.1016\/j.compchemeng.2022.107896","article-title":"AI-ML applications in bioprocessing: ML as an enabler of real time quality prediction in continuous manufacturing of mAbs","volume":"164","author":"Nikita","year":"2022","journal-title":"Comput. Chem. Eng."},{"key":"ref_46","first-page":"3596","article-title":"Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins","volume":"119","author":"Park","year":"2023","journal-title":"Biotechnol. Bioeng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1002\/bit.27706","article-title":"Control of surge tanks for continuous manufacturing of monoclonal antibodies","volume":"118","author":"Thakur","year":"2021","journal-title":"Biotechnol. Bioeng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.jprocont.2019.03.002","article-title":"Industrial batch process monitoring with limited data","volume":"77","author":"Tulsyan","year":"2019","journal-title":"J. Process Control"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1016\/B978-0-444-64241-7.50411-0","article-title":"Process variability source analysis for a multi-step bioprocess","volume":"44","author":"Jin","year":"2018","journal-title":"Comput.-Aided Chem. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13469","DOI":"10.1021\/acs.iecr.9b01175","article-title":"Classification and diagnosis of bioprocess cell growth productions using early-stage data","volume":"58","author":"Jin","year":"2019","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jesubalan, N.G., Thakur, G., and Rathore, A.S. (2023). Deep neural network for prediction and control of permeability decline in single pass tangential flow ultrafiltration in continuous processing of monoclonal antibodies. Front. Chem. Eng., 5.","DOI":"10.3389\/fceng.2023.1182817"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2013593","DOI":"10.1080\/19420862.2021.2013593","article-title":"Harnessing the potential of machine learning for advancing \u201cQuality by Design\u201d in biomanufacturing","volume":"14","author":"Walsh","year":"2022","journal-title":"mAbs"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1021\/acs.molpharmaceut.4c00804","article-title":"Machine learning models for predicting monoclonal antibody biophysical properties from molecular dynamics simulations and deep learning-based surface descriptors","volume":"22","author":"Wu","year":"2025","journal-title":"Mol. Pharm."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1021\/acs.molpharmaceut.0c01073","article-title":"Machine learning applied to determine the molecular descriptors responsible for the viscosity behavior of concentrated therapeutic antibodies","volume":"18","author":"Lai","year":"2021","journal-title":"Mol. Pharm."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e2026208","DOI":"10.1080\/19420862.2022.2026208","article-title":"Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics","volume":"14","author":"Lai","year":"2022","journal-title":"mAbs"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3161\u22123178","DOI":"10.1007\/s11095-014-1409-0","article-title":"Concentration dependent viscosity of monoclonal antibody solutions: Explaining experimental behavior in terms of molecular properties","volume":"31","author":"Li","year":"2014","journal-title":"Pharm. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"18601\u221218606","DOI":"10.1073\/pnas.1421779112","article-title":"In silico selection of therapeutic antibodies for development: Viscosity, clearance, and chemical stability","volume":"111","author":"Sharma","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"476\u2212489","DOI":"10.1080\/19420862.2017.1285479","article-title":"In-silico prediction of concentration- dependent viscosity curves for monoclonal antibody solutions","volume":"9","author":"Tomar","year":"2017","journal-title":"mAbs"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2169440","DOI":"10.1080\/19420862.2023.2169440","article-title":"Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks","volume":"15","author":"Schmitt","year":"2023","journal-title":"mAbs"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2303781","DOI":"10.1080\/19420862.2024.2303781","article-title":"Reduction of monoclonal antibody viscosity using interpretable machine learning","volume":"16","author":"Makowski","year":"2024","journal-title":"mAbs"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2483944","DOI":"10.1080\/19420862.2025.2483944","article-title":"Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning","volume":"17","author":"Kalejaye","year":"2025","journal-title":"mAbs"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e2163584","DOI":"10.1080\/19420862.2022.2163584","article-title":"Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features","volume":"15","author":"Harmalkar","year":"2023","journal-title":"mAbs"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"E486","DOI":"10.1073\/pnas.1613231114","article-title":"Mutational landscape of antibody variable domains reveals a switch modulating the interdomain conformational dynamics and antigen binding","volume":"114","author":"Koenig","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1007207","article-title":"Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces","volume":"15","author":"Warszawski","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1021\/acs.jctc.7b00125","article-title":"The Rosetta all-atom energy function for macromolecular modeling and design","volume":"13","author":"Alford","year":"2017","journal-title":"J. Chem. Theory Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1002\/bit.28239","article-title":"Machine learning approaches to root cause analysis, characterization, and monitoring of subvisible particles in monoclonal antibody formulations","volume":"119","author":"Greenblott","year":"2022","journal-title":"Biotechnol. Bioeng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1016\/j.xphs.2017.12.008","article-title":"Deep convolutional neural network analysis of flow imaging microscopy data to classify subvisible particles in protein formulations","volume":"107","author":"Calderon","year":"2018","journal-title":"J. Pharm. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"8299\u22128309","DOI":"10.1021\/acs.analchem.3c00650","article-title":"Rapid estimation of size-based heterogeneity in monoclonal antibodies by machine learning-enhanced dynamic light scattering","volume":"95","author":"Shrivastava","year":"2023","journal-title":"Anal. Chem."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1007\/s11095-017-2191-6","article-title":"High throughput prediction approach for monoclonal antibody aggregation at high concentration","volume":"34","author":"Zidar","year":"2017","journal-title":"Pharm. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ejpb.2019.05.017","article-title":"Application of interpretable artificial neural networks to early monoclonal antibodies development","volume":"141","author":"Gentiluomo","year":"2019","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1002\/bit.26881","article-title":"Review of advanced physical and data-driven models for dynamic bioprocess simulation: Case study of algae-bacteria consortium wastewater treatment","volume":"116","author":"Cong","year":"2019","journal-title":"Biotechnol. Bioeng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1007\/s00216-019-02110-8","article-title":"Neural network-based fingerprinting of monoclonal antibody aggregation using biolayer interferometry","volume":"412","author":"Budholiya","year":"2019","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/S1570-7946(09)70375-X","article-title":"Steady-state optimization of a continuous pharmaceutical process","volume":"7","author":"Gerogiorgis","year":"2009","journal-title":"Comput.-Aided Chem. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.omtm.2021.03.023","article-title":"Machine learning prediction of methionine and tryptophan photooxidation susceptibility","volume":"21","author":"Delmar","year":"2021","journal-title":"Mol. Ther. Methods Clin. Dev."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"28266","DOI":"10.1038\/s41598-025-13527-w","article-title":"Prediction of aggregation in monoclonal antibodies from molecular surface curvature","volume":"15","author":"Knez","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"6625","DOI":"10.1074\/jbc.M507783200","article-title":"Structure-function studies of two synthetic anti-vascular endothelial growth factor Fabs and comparison with the Avastin Fab","volume":"281","author":"Fuh","year":"2006","journal-title":"J. Biol. Chem."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.str.2011.03.019","article-title":"A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions","volume":"19","author":"Shapovalov","year":"2011","journal-title":"Structure"}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/18\/3313\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T04:10:40Z","timestamp":1790050240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/18\/3313"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":77,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["molecules31183313"],"URL":"https:\/\/doi.org\/10.3390\/molecules31183313","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]}}}