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Abstract Discovering antibodies with multiple beneficial properties is essential for therapeutic drug development, but experimentally identified antibodies require further optimization. Joint optimization remains challenging because sequence space is vast, annotations are sparse and property tradeoffs are common. Herein, we propose AbOptimizer, a reinforcement learning framework for multiproperty antibody optimization through parallel multisite sequence editing.
Conflicts of Interest The authors declare no conflict of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Supporting Information Filename Description adfm73678-sup-0001-SuppMat.docx4.4 MB Supporting File: adfm73678-sup-0001-SuppMat.docx References 1, , , et al., “Glioma,” Nature Reviews Disease Primers 1, no. 1 (2015): 1–18.
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