A Bayesian flow network with a semi-autoregressive causal mask and an auxiliary reinforcement learning term generates molecules with higher predicted docking scores than its training data, outperforming prior out-of-distribution baselines.
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Sampling Out-of-Distribution Chemical Spaces via Bayesian Flow
A Bayesian flow network with a semi-autoregressive causal mask and an auxiliary reinforcement learning term generates molecules with higher predicted docking scores than its training data, outperforming prior out-of-distribution baselines.