让大分子共享小分子的电子信息，低成本提升化学物理预测精度
Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
- 用小分子的电子数据迁移赋能大分子表示学习
- 在多个实验数据集上达到当前最优预测性能
- 适合需要高精度分子物理建模的研究者
针对分子结构的数据驱动化学研究，现有表示学习方法主要依赖原子级信息，难以刻画真实分子物理特性。虽然电子级信息能提供超越原子层面的化学知识，但在真实分子中获取电子级数据计算成本过高且常不可行。本文提出一种无需额外计算开销的方法，通过将小分子已有的电子级信息迁移到目标大分子，实现电子感知的分子表示学习。该方法在包含实验观测分子物理特性的多个基准数据集上取得了当前最优预测精度。HEDMoL 的源代码已公开于 https://github.com/ngs00/HEDMoL。
原文摘要 · Abstract (English)
Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL.
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