# LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction

arXiv: https://arxiv.org/abs/2106.14494 · HTML: https://mlsanity.com/paper/2106.14494/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016)
- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track](https://mlsanity.com/paper/2106.08279/) (2021)

## Built on by (most cited first, 6 of 6)

- [A New Perspective on the Effects of Spectrum in Graph Neural Networks](https://mlsanity.com/paper/2112.07160/) (2021)
- [Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models](https://mlsanity.com/paper/2310.13913/) (2023)
- [HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer](https://mlsanity.com/paper/2205.08055/) (2022)
- [Predicting Side Effect of Drug Molecules using Recurrent Neural Networks](https://mlsanity.com/paper/2305.10473/) (2023)
- [Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength](https://mlsanity.com/paper/2307.13909/) (2023)
- [Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM](https://mlsanity.com/paper/2504.20789/) (2025)

## Abstract (arXiv)

In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules. Our solution, Lite Geometry Enhanced Molecular representation learning (LiteGEM) achieves a mean absolute error (MAE) of 0.1204 on the test set with the help of deep graph neural networks and various self-supervised learning tasks. The code of the framework can be found in https://github.com/PaddlePaddle/PaddleHelix/tree/dev/competition/kddcup2021-PCQM4M-LSC/.