用物理方程约束抗体生成，让设计更真实且功能更强。
FP-AbDiff: Improving Score-based Antibody Design by Capturing Nonequilibrium Dynamics through the Underlying Fokker-Planck Equation
- 基于福克-普朗克方程构建动力学一致性损失，确保生成过程符合物理规律。
- 在全新CDR-H3设计中，均方根偏差达0.99 Å，比前代模型提升25%。
- 适合需要高精度、可解释抗体设计的药物研发人员使用。
计算抗体设计在治疗药物发现中前景广阔，但现有生成模型受限于两大核心问题：(i) 动力学不一致导致结构物理上不可行，(ii) 因数据稀缺和结构偏见导致泛化能力差。我们提出FP-AbDiff，首个在整条生成轨迹中强制遵循福克-普朗克方程（FPE）物理规律的抗体生成模型。该方法在CDR几何空间（R^3 × SO(3)）的混合流形上最小化新颖的FPE残差损失，促使局部学习的去噪得分整合为全局一致的概率流。这一物理信息正则化项与最先进的SE(3)-等变扩散框架中的深层生物学先验协同集成。在RAbD基准上的严格评估表明，FP-AbDiff达到新最佳性能。在从头设计CDR-H3时，其均方根偏差为0.99 Å（相对于可变区叠加），较前代最优模型AbX提升25%，并实现39.91%的最高接触氨基酸恢复率。在更具挑战性的六个CDR联合设计任务中，模型持续展现更优几何精度，全链均方根偏差降低约15%，尤其在功能关键的CDR-H3环上达到45.67%的最高全链氨基酸恢复率。通过对齐生成动态与物理法则，FP-AbDiff显著提升鲁棒性与泛化能力，建立了一种原理清晰、物理可信且功能可行的抗体设计范式。
原文摘要 · Abstract (English)
Computational antibody design holds immense promise for therapeutic discovery, yet existing generative models are fundamentally limited by two core challenges: (i) a lack of dynamical consistency, which yields physically implausible structures, and (ii) poor generalization due to data scarcity and structural bias. We introduce FP-AbDiff, the first antibody generator to enforce Fokker-Planck Equation (FPE) physics along the entire generative trajectory. Our method minimizes a novel FPE residual loss over the mixed manifold of CDR geometries (R^3 x SO(3)), compelling locally-learned denoising scores to assemble into a globally coherent probability flow. This physics-informed regularizer is synergistically integrated with deep biological priors within a state-of-the-art SE(3)-equivariant diffusion framework. Rigorous evaluation on the RAbD benchmark confirms that FP-AbDiff establishes a new state-of-the-art. In de novo CDR-H3 design, it achieves a mean Root Mean Square Deviation of 0.99 Å when superposing on the variable region, a 25% improvement over the previous state-of-the-art model, AbX, and the highest reported Contact Amino Acid Recovery of 39.91%. This superiority is underscored in the more challenging six-CDR co-design task, where our model delivers consistently superior geometric precision, cutting the average full-chain Root Mean Square Deviation by ~15%, and crucially, achieves the highest full-chain Amino Acid Recovery on the functionally dominant CDR-H3 loop (45.67%). By aligning generative dynamics with physical laws, FP-AbDiff enhances robustness and generalizability, establishing a principled approach for physically faithful and functionally viable antibody design.
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