Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical properties without 3D equilibrium structures. This is practically favorable since obtaining 3D equilibrium structures requires extremely expensive calculations. In this work, we design a deep graph neural network to predict quantum properties by directly learning from 2D molecular graphs. In addition, we propose a 3D graph neural network to learn from low-cost conformer sets, which can be obtained with open-source tools using an affordable budget. We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules. Final evaluation results reveal that we are one of the winners with a mean absolute error of 0.1235 on the holdout test set. Our implementation is available as part of the MoleculeX package (https://github.com/divelab/MoleculeX).
@article{arxiv.2106.08551,
title = {Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks},
author = {Meng Liu and Cong Fu and Xuan Zhang and Limei Wang and Yaochen Xie and Hao Yuan and Youzhi Luo and Zhao Xu and Shenglong Xu and Shuiwang Ji},
journal= {arXiv preprint arXiv:2106.08551},
year = {2021}
}
Comments
One of the winners of 2021 KDD Cup on OGB Large-Scale Challenge