电子信息引导的粗粒化分子表示学习用于现实世界分子物理
Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
AI总结：
HEDMoL通过利用小分子电子信息提升大分子表示学习，实现对现实世界分子物理的高效预测。
摘要：
为加速数据驱动的化学研究，已开发出各种分子结构的表示学习方法。然而，现有方法的表示能力本质上仅限于原子级信息，这不足以描述现实世界中的分子物理。尽管电子级信息可以提供超越原子级信息的关于化学化合物的基本知识，但在现实世界分子中获取电子级信息是计算上不现实的，有时甚至是不可能的。我们提出了一种无需额外计算成本的学习电子信息引导的分子表示的方法，通过将易于获得的小分子电子级信息转移到我们感兴趣的大型分子上。所提出的方法在包含实验观测分子物理的广泛基准数据集上实现了最先进的预测准确性。HEDMoL的源代码可在https://github.com/ngs00/HEDMoL上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
