评估带长程作用的通用机器学习势在生物分子模拟中的表现
Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
- 用等变消息传递架构训练模型，显式加入长程色散与静电作用
- 大模型提升基准测试准确率，但模拟性能不一致，训练数据配比影响结果
- 对Trp-cage蛋白，长程静电增加构象多样性，适合关注蛋白质动态的研究者
通用机器学习势有望在不同化学组成和振动自由度下保持高精度，但在生物分子模拟中应用仍不充分。本研究系统评估了基于SPICE-v2数据集训练的等变消息传递架构，对比包含与不包含显式长程色散和静电作用的情况。通过分析模型规模、训练数据组成及电荷处理方式，在分布内与分布外基准数据集上进行评估，并开展液态水、含NaCl水溶液以及丙氨酸三肽、Trp-cage小蛋白和Crambin的分子模拟。虽然更大模型在基准测试中提升精度，但该趋势未在模拟性质中一致体现；预测结果依赖于训练数据组成。长程静电在各类体系中无系统性影响，但在Trp-cage中显著提高构象变异性。结果表明，当前数据集不平衡与评估方法不成熟制约了通用机器学习势在生物分子模拟中的实际应用。
原文摘要 · Abstract (English)
Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remains underexplored. This work systematically evaluates equivariant message-passing architectures trained on the SPICE-v2 dataset with and without explicit long-range dispersion and electrostatics. We assess the impact of model size, training data composition, and electrostatic treatment across in- and out-of-distribution benchmark datasets, as well as molecular simulations of bulk liquid water, aqueous NaCl solutions, and biomolecules, including alanine tripeptide, the mini-protein Trp-cage, and Crambin. While larger models improve accuracy on benchmark datasets, this trend does not consistently extend to properties obtained from simulations. Predicted properties also depend on the composition of the training dataset. Long-range electrostatics show no systematic impact across systems. However, for Trp-cage, their inclusion yields increased conformational variability. Our results suggest that imbalanced datasets and immature evaluation practices currently challenge the applicability of universal machine-learned potentials to biomolecular simulations.
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