LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
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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/.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 28 Jun 2021.
Builds on
8 of 15 references.
References · 8
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2017Neural Message Passing for Quantum Chemistrycited by 3,460
- 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
- 2020DeeperGCN: All You Need to Train Deeper GCNscited by 249
- 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
- 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
- 2021First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Trackcited by 10
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 1.
6 papers.
- 2021A New Perspective on the Effects of Spectrum in Graph Neural Networkscited by 24
- 2023Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Modelscited by 5
- 2022HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfercited by 1
- 2023Predicting Side Effect of Drug Molecules using Recurrent Neural Networkscited by 1
- 2023Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strengthcited by 0
- 2025Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTMcited by 0
- 2025Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM29 Apr 2025
- 2023Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models21 Oct 2023
- 2023Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength26 Jul 2023
- 2023Predicting Side Effect of Drug Molecules using Recurrent Neural Networks17 May 2023
- 2022HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer17 May 2022
- 2021A New Perspective on the Effects of Spectrum in Graph Neural Networks14 Dec 2021