GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling
GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling
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DOI：
10.1021/acs.jctc.1c00735
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发表时间：
2021-12-14
影响因子：
5.5
通讯作者：
Laughton, Charles A.
中科院分区：
文献类型：
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作者：
Louison, Keverne A.;Dryden, Ian L.;Laughton, Charles A.
我们描述了一种基于机器学习方法在不同分辨率级别之间转换分子模型的通用方法。该方法使用两个分辨率级别的匹配模型集进行训练，但仅需要粒子的坐标，不需要辅助信息（例如，子结构的模板、定义的映射或分子力学力场）。经过训练后，该方法可以在任一方向上以相同的便利性在两个分辨率水平之间转换系统的进一步分子模型。
We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility.