发表机构
Technical University of Munich（慕尼黑技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对生物分子系统建模中机器学习原子间势推理慢的问题，提出由等变图神经网络参数化、基于从头算和实验标签训练的可转移水隐式网络（TWIN），该模型可转移性强，性能优，为生物分子系统高效建模提供了可能。
AI 中文摘要
机器学习原子间势（MLP）革新了原子建模，有望取代传统方法如密度泛函理论（DFT）。然而，MLP的推理时间比经典力场慢几个数量级，阻碍了生物分子系统的实际应用。隐式溶剂MLP可解决此问题，但面临粗粒度建模的数据挑战。此前方法依赖经验力场数据，限制了MLP精度。本文介绍可转移水隐式网络（TWIN），由等变图神经网络完全参数化，仅基于从头算和实验标签训练。实验证明TWIN在药物样分子、肽和蛋白质间具有可转移性，在多个基准测试中表现出色，优于先前模型，且与基于DFT的显式溶剂MLP效果相近，时间步长评估快两个数量级，为水环境中生物分子系统的高效从头算建模铺平道路。
英文摘要
Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). However, inference time of MLPs is orders of magnitude slower than that of classical force fields, hindering real-world applications for biomolecular systems that require timescales of microseconds and beyond. Implicit solvent MLPs can address this issue, but are faced with data challenges associated with coarse-grained modeling. Consequently, previous approaches relied on empirical force field data, thereby inherently limiting the MLP's accuracy. Here, we introduce the Transferable Water Implicit Network (TWIN), an implicit water MLP parametrized entirely by an Equivariant Graph Neural Network and trained solely on ab initio and experimental labels. We demonstrate TWIN's transferability across drug-like molecules, peptides, and proteins, achieving excellent results on ab initio and experimental crystallographic and NMR benchmarks, consistently outperforming previous machine-learning-based implicit solvent or coarse-grained models. Furthermore, TWIN closely matches DFT-based explicit solvent MLPs while providing a two-order-of-magnitude faster timestep evaluation, paving the way for efficient ab initio-level modeling of biomolecular systems in aqueous environments.
