Reduction of monoclonal antibody viscosity using interpretable machine learning by Emily K. Makowski, Hsin-Ting Chen, Tiexin Wang, Lina Wu, Jie Huang, Marissa Mock, Patrick Underhill, Emma Pelegri-O'Day, Erick Maglalang, Dwight Winters, Peter M. Tessier

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Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies by Marissa Mock, Alex W. Jacobitz, Christopher James Langmead, Athena Sudom, Daniel Yoo, Sara C. Humphreys, Mai Alday, Larysa Alekseychyk, Nicolas Angell, Vivian Bi, Hannah Catterall, Chen-Chun Chen, Hui-Ting Chou, Kip P. Conner, Kevin D. Cook, Ana R. Correia, Andrew Dykstra, Sudipa Ghimire-Rijal, Kevin Graham, Peter Grandsard, Joon Huh, John O. Hui, Mani Jain, Victoria Jann, Lei Jia, Sheree Johnstone, Neelam Khanal, Carl Kolvenbach, Linda Narhi, Rupa Padaki, Emma M. Pelegri-O'Day, Wei Qi, Vladimir Razinkov, Austin J. Rice, Richard Smith, Christopher Spahr, Jennitte Stevens, Yax Sun, Veena A. Thomas, Sarah van Driesche, Robert Vernon, Victoria Wagner, Kenneth W. Walker, Yangjie Wei, Dwight Winters, Melissa Yang, Iain D. G. Campuzano

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