首个通用可调的图神经网络粗粒化力场，提升生物大分子模拟精度。
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
- 基于图神经网络自适应粗粒化，无需硬编码规则，自动优化降维质量。
- 在多种生物系统上实现稳定轨迹，训练噪声降低显著，提升模型可靠性。
- 适配蛋白质、RNA、脂质系统，适合需要通用力场的分子模拟研究者。
针对分子系统的粗粒化（CG）力场方法是模拟大型生物大分子的关键工具，对生物分子体系表征至关重要。尽管近年来基于深度学习（DL）的全原子力场模型取得显著进展，但现有基于深度学习的粗粒化模拟方法仍存在明显局限。本文提出首个可迁移的深度学习驱动粗粒化力场方法，适用于广泛生物系统，不局限于特定体系。其核心算法不依赖硬编码规则，通过优化输出粗粒化体系以最小化真实粗粒化力中的统计噪声，显著提升模型训练效果。该力场模型首次基于MACE架构，并在新构建的定制数据集上训练，该数据集采用大规模生物系统分片段方法生成，涵盖蛋白质、RNA和脂质化学。我们验证了该模型在分子动力学模拟中可生成稳定且定性准确的轨迹，同时讨论了其在部分体系中可靠性有限的情况。
原文摘要 · Abstract (English)
Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of biomolecular systems. While state-of-the-art deep learning (DL)-based models for all-atom force fields have improved immensely over recent years, we observe and analyse significant limitations of the currently available approaches for DL-based CG simulations. In this work, we present the first transferable DL-based CG force field approach (i.e., not specific to only one narrowly defined system type) applicable to a wide range of biosystems. To achieve this, our CG algorithm does not rely on hard-coded rules and is tuned to output coarse-grained systems optimised for minimal statistical noise in the ground truth CG forces, which results in significant improvement of model training. Our force field model is also the first CG variant that is based on the MACE architecture and is trained on a custom dataset created by a new approach based on the fragmentation of large biosystems covering protein, RNA and lipid chemistry. We demonstrate that our model can be applied in molecular dynamics simulations to obtain stable and qualitatively accurate trajectories for a variety of systems, while also discussing cases for which we observe limited reliability.
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