用基数分解实现分子自由能高效估算，速度提升40倍且通用性强。
CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation
- 通过基数分解将3D坐标转为混合序列，实现粗到细的自回归建模
- 在未见系统上达到经典方法精度，推理速度提升约40倍
- 无需化学路径即可估计任意系统的绝对自由能，适合药物设计
估算自由能差值可量化分子相互作用的热力学偏好，对化学与药物研发至关重要。尽管进展显著，现有方法仍存在关键局限：经典计算方法因依赖大量分子动力学模拟而成本高昂；深度学习方法受限于表达力不足的生成模型或与特定系统绑定的输入维度，导致泛化能力弱。为此，我们提出CARD，一种生成框架，采用新型基数分解，将3D坐标双射转换为混合离散-连续序列，实现增强表达力的粗到细自回归建模。值得注意的是，该模型对应零自由能分布，可作为任意系统绝对自由能计算的提议分布，无需依赖化学路径。在多样任务上的实验表明，CARD在未见系统上达到经典计算方法的精度，同时推理速度提升约40倍。
原文摘要 · Abstract (English)
Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Despite fruitful progress, existing methods still face key limitations: classical computational approaches remain prohibitively expensive due to their reliance on extensive molecular dynamics simulations, while deep learning-based methods are constrained by either less-expressive generative models or input dimensions tied to a specific system, resulting in negligible generalization. To address these challenges, we propose CARD, a generative framework that employs a novel radix-based decomposition to bijectively convert 3D coordinates into mixed discrete-continuous sequences, enabling coarse-to-fine autoregressive modeling with enhanced expressiveness. Notably, the model corresponds to a distribution with zero free energy, serving as a proposal for absolute free energy computation of arbitrary systems without relying on alchemical pathways. Experiments across diverse tasks demonstrate that CARD matches the accuracy of classical computational methods on unseen systems with diverse topologies, while achieving an approximately 40-fold speedup in inference.
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