@inproceedings{
na2023electronderived,
title={Electron-Derived Molecular Representation Learning for Real-World Molecular Physics},
author={Gyoung S. Na and Chanyoung Park},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=kZQ0iFqhEQ}
}
Various representation learning methods for molecular structures have been devised to accelerate data-driven drug and materials discovery. 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 new method for learning electron-derived molecular representations without additional computation costs by transferring pre-calculated 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.
Electron-Derived Molecular Representation Learning for Real-World Molecular Physics
Gyoung S. Na Chanyoung Park