mlsanityData to 7 Oct 2026

ChiENN: Embracing Molecular Chirality with Graph Neural Networks

arXiv 2307.02198 ↗First posted 5 Jul 2023cs.LG q-bio.QMcited by 6

Not on the map. Why?

Abstract (arXiv)

Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish between the 3D graph of a chemical compound and its mirror image (enantiomer). The ability to distinguish between enantiomers is important especially in drug discovery because enantiomers can have very distinct biochemical properties. In this paper, we propose a theoretically justified message-passing scheme, which makes GNNs sensitive to the order of node neighbors. We apply that general concept in the context of molecular chirality to construct Chiral Edge Neural Network (ChiENN) layer which can be appended to any GNN model to enable chirality-awareness. Our experiments show that adding ChiENN layers to a GNN outperforms current state-of-the-art methods in chiral-sensitive molecular property prediction tasks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

20 of 42 references.

References · 20

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2017Deep Setscited by 1,394
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  5. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  6. 2020Directional Message Passing for Molecular Graphscited by 556
  7. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  8. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  9. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  10. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
  11. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  12. 2020Molecule Attention Transformercited by 92
8 more
  1. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  2. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  3. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
  4. 2019Mol-CycleGAN - a generative model for molecular optimizationcited by 66
  5. 2022Unified 2D and 3D Pre-Training of Molecular Representationscited by 45
  6. 2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
  7. 2020Message Passing Networks for Molecules with Tetrahedral Chiralitycited by 14
  8. 2021Geometric Transformer for End-to-End Molecule Properties Predictioncited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024On the Completeness of Invariant Geometric Deep Learning Modelscited by 11
  2. 2024RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNetscited by 10
  3. 2026Learning Molecular Chirality via Chiral Determinant Kernelscited by 2
  4. 2025Toward bilipshiz geometric modelscited by 1
  5. 2025Chi-Geometry: A Library for Benchmarking Chirality Prediction of GNNscited by 0
  6. 2026GSF-chi: Global Stereochemical Fields for Chiral Graph Transformerscited by 0