mlsanityData to 7 Oct 2026

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

arXiv 2106.08551 ↗First posted 16 Jun 2021cs.LGcited by 10

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Abstract (arXiv)

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).

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

21 of 28 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  9. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  10. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  11. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  12. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
9 more
  1. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  2. 2020Directional Message Passing for Molecular Graphscited by 556
  3. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  4. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  5. 2020Towards Deeper Graph Neural Networkscited by 296
  6. 2020DeeperGCN: All You Need to Train Deeper GCNscited by 249
  7. 2020On Learning Sets of Symmetric Elementscited by 97
  8. 2021ForceNet: A Graph Neural Network for Large-Scale Quantum Calculationscited by 58
  9. 2020Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discoverycited by 46

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 3; 2025: 1.

10 papers.

  1. 2022Multimodal learning with graphscited by 54
  2. 2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
  3. 2024Complete and Efficient Graph Transformers for Crystal Material Property Predictioncited by 34
  4. 2021Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphscited by 31
  5. 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
  6. 2023A Latent Diffusion Model for Protein Structure Generationcited by 28
  7. 2024Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networkscited by 4
  8. 2024Bridging Geometric States via Geometric Diffusion Bridgecited by 2
  9. 20233D Molecular Geometry Analysis with 2D Graphscited by 1
  10. 2025The impact of conformer quality on learned representations of molecular conformer ensemblescited by 1