用贝叶斯流模型生成超越训练数据的新分子，提升药物设计效率。
Sampling Out-of-Distribution Chemical Spaces via Bayesian Flow
- 基于贝叶斯流网络与强化学习，实现高效分子采样。
- 半自回归策略使生成分子性质超越现有模型。
- 适合药物发现领域研究人员参考使用。
生成属性优于训练数据分布的新分子（即分布外生成）对全新药物设计至关重要。然而，基于分布学习的模型（如扩散模型）因需紧密拟合训练数据分布，难以应对该挑战。本文表明，贝叶斯流网络（尤其是ChemBFN模型）可内生生成高质量分布外分子，适用于多种场景。通过在ChemBFN中引入强化学习策略，并采用类常微分方程求解器的可控生成过程，加速采样。更重要的是，训练与推理阶段引入半自回归策略，显著提升模型性能，超越当前最优模型。论文还提供了带有半自回归策略的ChemBFN在分布外生成上的理论分析。
原文摘要 · Abstract (English)
Generating novel molecules with higher properties than the training space, namely the out-of-distribution generation, is important for de novo drug design. However, it is not easy for distribution learning-based models, for example diffusion models, to solve this challenge as these methods are designed to fit the distribution of training data as close as possible. In this paper, we show that Bayesian flow network, especially ChemBFN model, is capable of intrinsically generating high quality out-of-distribution samples that meet several scenarios. A reinforcement learning strategy is added to the ChemBFN and a controllable ordinary differential equation solver-like generating process is employed that accelerate the sampling processes. Most importantly, we introduce a semi-autoregressive strategy during training and inference that enhances the model performance and surpass the state-of-the-art models. A theoretical analysis of out-of-distribution generation in ChemBFN with semi-autoregressive approach is included as well.
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