A curated list of resources for molecular graph representation learning (Stay tuned).
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | Motif-based Graph Representation Learning with Application to Chemical Molecules. | arXiv 2022 | Link | Link |
| 2022 | Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks. | ICML 2022 | Link | Link |
| 2022 | Chemical-reaction-aware molecule representation learning. | ICLR 2022 | Link | Link |
| 2022 | Spherical message passing for 3d molecular graphs. | ICLR 2022 | Link | Link |
| 2021 | Deep Molecular Representation Learning via Fusing Physical and Chemical Information | NeurIPS 2021 | Link | - |
| 2020 | Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures (MXMNet) | 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 |
| 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 |