This repo is the official implementation of GeoMFormer: A General Architecture for Geometric Molecular Representation Learning, which was presented at ICML 2024.
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen*, Shengjie Luo*, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang
Contact: Tianlang Chen (chentl@stanford.edu), Shengjie Luo (luosj@stu.pku.edu.cn)
We developed GeoMFormer, a Transformer-based geometric molecular model, capable of effectively making both invariant and equivariant predictions with high accuracy. By simultaneously and comprehensively modeling interatomic interactions within and across feature spaces in a unified manner, GeoMFormer demonstrates strong performance across diverse data modalities, scales, and tasks.
We empirically investigate the performance of GeoMFormer across a range of extensive tasks. Below, we present a selection of these tasks. For complete results, please refer to the full paper.
If you find our work useful, please consider citing it:
@inproceedings{
chen2024geomformer,
title={Geo{MF}ormer: A General Architecture for Geometric Molecular Representation Learning},
author={Tianlang Chen and Shengjie Luo and Di He and Shuxin Zheng and Tie-Yan Liu and Liwei Wang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=Y5Zi59N265}
}