发表机构
AXXX（AXXX）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Harmony调和扭转扩散框架，通过对齐得分参数化与扩散过程几何，在PDBBind、PoseBusters基准及EBNA1、KRAS G12D案例中提升了蛋白质-配体柔性对接的精度与有效性。
AI 中文摘要
分子对接需要联合推理配体构象和蛋白质柔性。大多数基于扩散的对接模型使用通用欧几里得头预测扭转更新，忽略了角变量的周期性几何，这种不匹配在柔性对接中尤为受限——配体构象与口袋侧链会共同适配以形成结合复合物。本文提出Harmony，一种用于蛋白质-配体柔性对接的调和扭转扩散框架。Harmony将配体和侧链扭转得分场参数化为圆上学习到的调和势的导数，其噪声水平依赖性由环面上方差爆炸扩散的热半群解析提供。该构造明确了周期性，为模型提供了对旋转体运动的频率感知归纳偏置。在PDBBind基准上，Harmony较近期柔性对接方法提升了配体构象精度和口袋全原子重构；在PoseBusters上，它改善了生成复合物的物理有效性。对EBNA1和KRAS G12D的案例研究分别展示了该方法在极性结合位点和浅结合位点上的表现。这些结果表明，使得分参数化与扩散过程的几何结构对齐，是提升柔性对接的一种简单有效的手段。
英文摘要
Molecular docking requires reasoning jointly about ligand pose and protein flexibility. Most diffusion-based docking models predict torsional updates with generic Euclidean heads that ignore the periodic geometry of angular variables. This mismatch is especially limiting in flexible docking, where ligand conformations and pocket side chains co-adapt to form the bound complex. Here, we introduce Harmony, a harmonic torsional diffusion framework for flexible protein-ligand docking. Harmony parameterizes ligand and side-chain torsional score fields as derivatives of learned harmonic potentials on the circle, whose noise-level dependence is supplied analytically by the heat semigroup of variance-exploding diffusion on the torus. This construction makes periodicity explicit and gives the model a frequency-aware inductive bias over rotameric motion. On the PDBBind benchmark, Harmony improves ligand pose accuracy and pocket all-atom reconstruction over recent flexible docking methods. On PoseBusters, it improves the physical validity of generated complexes. Case studies on EBNA1 and KRAS G12D illustrate the method's behavior on a polar and a shallow binding site, respectively. Together, these results indicate that aligning the score parameterization with the geometry of the diffusion process is a simple and effective lever for improving flexible docking.
CommentsAccepted at the 2026 Workshop on Generative and Agentic AI for Biology (ICML 2026)
