TWIN这个新模型可让药物和蛋白质模拟达到从头算精度，而且比DFT快100倍，值得做分子模拟的人看看。
TWIN（Transferable Water Implicit Network）是一种基于等变图神经网络参数化的隐式水机器学习势，完全使用从头算和实验标签训练，无需经验力场数据。在药物分子、肽和蛋白质上展现了可转移性，在从头算和实验晶学及NMR基准上优于以往的机器学习隐式溶剂或粗粒化模型。TWIN在匹配DFT显式溶剂MLP精度的同时，时间步评估快了两个数量级，为生物分子系统的高效从头算水平模拟铺平了道路。
Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy
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.