AI 中文总结
研究蛋白质-配体结合动力学难计算问题，基于AIMMD框架，结合路径采样与无描述符图神经网络，用静态偏置势改进收敛，无需特定系统设置，能高效准确计算动力学并重建机制，为探索结构-动力学关系奠定基础。
AI 中文摘要
蛋白质-配体结合系统的动力学越来越被认为是药物疗效的关键决定因素，但比结合亲和力更难计算。现有动力学方法要么沿集体变量（CV）使动力学有偏差，需要针对特定系统精心设计CV，要么使用路径采样，虽动力学无偏差，但难以从深自由能阱中收敛速率且常依赖手工设计的描述符。我们结合两者优点，基于分子机制发现人工智能（AIMMD）路径采样框架，提出一种以适度计算成本和最小微调计算一般配体解离问题准确动力学的方法。避免特征工程，用单个无描述符、等变图神经网络对所有系统的反应几率进行建模，还用静态、盆地受限偏置势部分展平深束缚态阱。在跨越约17个数量级停留时间的主客体和蛋白质-配体系统中，该方法稳健恢复与参考值和实验值一致的速率，还能重建潜在解离机制。此外，准确速率不需要全局准确的反应几率模型，即使在低数据训练模式下也能进行高效动力学估计。该方法几乎不需要特定系统设置，为结合动力学提供了一条高效且广泛通用的途径，其共享的反应几率架构为探索药物发现中配体系列的结构-动力学关系奠定了关键基础。
英文摘要
The kinetics of protein-ligand binding systems are increasingly recognized as a key determinant of drug efficacy, yet remain far harder to compute than binding affinities. Existing kinetics methods either bias the dynamics along a collective variable (CV), demanding careful system-specific CV design, or use path sampling, which keeps the dynamics unbiased but can struggle to converge rates out of deep free-energy wells and often relies on hand-engineered descriptors. By combining the `best of both worlds', we propose a method to compute accurate kinetics for general ligand-unbinding problems at modest computational expense and minimal fine tuning, building on the AI for Molecular Mechanism Discovery (AIMMD) path sampling framework. To avoid the need for feature engineering, we opt for modelling the committor with a single descriptor-free, equivariant graph neural network shared across all systems. We also partially flatten deep bound-state wells with a static, basin-restricted bias potential. This improves convergence by lifting the path sampling state boundary out of regions, where the committor is hard to learn, while leaving the reactive region strictly unbiased. Across host-guest and protein-ligand systems spanning roughly 17 orders of magnitude in residence time, the method robustly recovers rates in line with reference and experimental values. Simultaneously, and without further sampling, it also reconstructs the underlying unbinding mechanisms. We additionally find that accurate rates do not require globally accurate committor models, allowing for efficient kinetics estimation even in a low-data training regime. Requiring little system-specific setup, our approach offers an efficient and broadly generalizable route to binding kinetics, and its shared committor architecture lays crucial groundwork for probing structure-kinetics relationships across ligand series in drug discovery.
