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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.
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