标题： 通过线性张量化四边形注意力的可扩展且量子精确的生物分子力场基础模型 显示英文标题

标题： A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention

摘要： 精确的原子级生物分子模拟对于疾病机制的理解、药物发现和生物材料设计至关重要，但现有的模拟方法存在显著的局限性。 经典力场方法效率高，但在许多化学和生物过程中至关重要的过渡态和精细构象细节方面缺乏准确性。 量子力学（QM）方法精度高，但对于大规模或长时间的模拟计算上不可行。 基于人工智能的力场（AIFFs）旨在实现与QM相当的精度，同时保持效率，但在多体建模复杂性、精度和速度之间难以平衡，通常受到有限训练数据和泛化能力验证不足的限制。 为克服这些挑战，我们引入了LiTEN，这是一种具有张量化四边形注意力（TQA）的新颖等变神经网络。 TQA通过向量运算重新参数化高阶张量特征，以线性复杂度高效地建模三体和四体相互作用，避免了昂贵的球面谐波计算。 在LiTEN的基础上，LiTEN-FF是一个强大的AIFF基础模型，使用广泛的nablaDFT数据集进行预训练，以实现广泛的化学泛化，并在SPICE上进行微调，以实现准确的溶剂化系统模拟。 LiTEN在rMD17、MD22和Chignolin的大多数评估子集上实现了最先进的（SOTA）性能，优于MACE、NequIP和EquiFormer等领先模型。 LiTEN-FF迄今为止提供了最全面的下游生物分子建模任务套件，包括QM级别的构象搜索、几何优化和自由能表面构建，同时在大型生物分子（约1000个原子）上的推理速度比MACE-OFF快10倍。 总之，我们提出了一种物理基础坚实、高效的高度框架，推动了复杂生物分子建模的发展，为药物发现及相关应用提供了一个多功能的基础。 显示英文摘要

摘要： Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significant limitations. Classical force fields are efficient but lack accuracy for transition states and fine conformational details critical in many chemical and biological processes. Quantum Mechanics (QM) methods are highly accurate but computationally infeasible for large-scale or long-time simulations. AI-based force fields (AIFFs) aim to achieve QM-level accuracy with efficiency but struggle to balance many-body modeling complexity, accuracy, and speed, often constrained by limited training data and insufficient validation for generalizability. To overcome these challenges, we introduce LiTEN, a novel equivariant neural network with Tensorized Quadrangle Attention (TQA). TQA efficiently models three- and four-body interactions with linear complexity by reparameterizing high-order tensor features via vector operations, avoiding costly spherical harmonics. Building on LiTEN, LiTEN-FF is a robust AIFF foundation model, pre-trained on the extensive nablaDFT dataset for broad chemical generalization and fine-tuned on SPICE for accurate solvated system simulations. LiTEN achieves state-of-the-art (SOTA) performance across most evaluation subsets of rMD17, MD22, and Chignolin, outperforming leading models such as MACE, NequIP, and EquiFormer. LiTEN-FF enables the most comprehensive suite of downstream biomolecular modeling tasks to date, including QM-level conformer searches, geometry optimization, and free energy surface construction, while offering 10x faster inference than MACE-OFF for large biomolecules (~1000 atoms). In summary, we present a physically grounded, highly efficient framework that advances complex biomolecular modeling, providing a versatile foundation for drug discovery and related applications.