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The ensemble-conditioned guidance framework is introduced, a framework which conditions 3D molecular generative models on both axes simultaneously and applies to two practical drug discovery tasks, dual-target binder design and active-state-selective agonist design, where conditioning on the additional state improves t...
A flexible reinforcement learning method is introduced for flow-matching based generative models, allowing the velocity field to be refined according to a user-defined reward function, enabling dynamic fine-tuning and joint optimization of features, outperforming baseline approaches in diverse optimization scenarios.
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