A curated list of resources for molecular graph representation learning (Stay tuned).
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | Learning Substructure Invariance for Out-of-Distribution Molecular Representations | NeurIPS 2022 | Link | Link |
| 2022 | Motif-based Graph Representation Learning with Application to Chemical Molecules | arXiv 2022 | Link | Link |
| 2022 | Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs | arXiv 2022 | link | - |
| 2022 | ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs (ComENet) | NeurIPS 2022 | link | link |
| 2022 | Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks | ICML 2022 | Link | Link |
| 2022 | Chemical-reaction-aware molecule representation learning (MoIR) | ICLR 2022 | Link | Link |
| 2022 | Spherical message passing for 3d molecular graphs. (SphereNet) | ICLR 2022 | Link | Link |
| 2022 | An autoregressive flow model for 3d molecular geometry generation from scratch (G-SphereNet) | ICLR 2022 | link | link |
| 2022 | SE(3) Equivariant Graph Neural Networks with Complete Local Frames (ClofNet) | ICML 2022 | link | link |
| 2021 | E(n) Equivariant Graph Neural Networks (EGNNs) | ICML 2021 | link | link |
| 2021 | Deep Molecular Representation Learning via Fusing Physical and Chemical Information | NeurIPS 2021 | Link | - |
| 2021 | Motif-based Graph Self-Supervised Learning for Molecular Property Prediction (MGSSL) | NeurIPS 2021 | link | link |
| 2021 | MOTIF-Driven Contrastive Learning of Graph Representations (MOTIF) | AAAI 2021 | link | - |
| 2021 | MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph (MoCL) | KDD 2021 | link | link |
| 2021 | Motif-Driven Contrastive Learning of Graph Representations (MICRO-Graph) | arXiv 2021 | link | - |
| 2020 | Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures (MXMNet) | NeurIPS 2020 | Link | Link |
| 2020 | Self-supervised graph transformer on large-scale molecular data (GROVER) | NeurIPS 2020 | link | link |
| 2020 | SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks | NeurIPS 2020 | link | link |
| 2020 | Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules (DimeNet++) | NeurIPS 2020 workshop | Link | Link |
| 2020 | Directional Message Passing for Molecular Graphs (DimeNet) | ICLR 2020 | Link | Link |
| 2020 | Strategies for pre-training graph neural networks | ICLR 2020 | link | link |
| 2020 | Hierarchical inter-message passing for learning on molecular graphs (HIMP) | ICML 2020 workshop | link | link |
| 2019 | Analyzing Learned Molecular Representations for Property Prediction | JCIM 2019 | link | - |
| 2019 | Molecular property prediction: A multilevel quantum interactions modeling perspective (MGCN) | AAAI 2019 | link | - |
| 2017 | Quantum-chemical insights from deep tensor neural networks (DTNN) | Nature Communications 2017 | link | link |
| 2017 | Neural message passing for quantum chemistry (MPNN) | ICML 2017 | Link | Link |
| 2017 | SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (SchNet) | NeurIPS 2017 | Link | Link |
| 2015 | Convolutional Networks on Graphs for Learning Molecular Fingerprints | NeurIPS 2015 | link | link |