% Key papers for Molecular graph pretraining (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_junction_36765,
  title = {{Junction Tree Variational Autoencoder for Molecular Graph Generation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1903},
}

@inproceedings{neurips2020_self_60801,
  title = {{Self-Supervised Graph Transformer on Large-Scale Molecular Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17230},
}

@inproceedings{neurips2018_constrained_56965,
  title = {{Constrained Graph Variational Autoencoders for Molecule Design}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11748},
}

@inproceedings{neurips2018_graph_57185,
  title = {{Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11620},
}

@inproceedings{iclr2020_graphaf_29582,
  title = {{GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=S1esMkHYPr},
}

@inproceedings{iclr2022_pre_31842,
  title = {{Pre-training Molecular Graph Representation with 3D Geometry}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6888},
}

@inproceedings{aaai2019_nevae_100195,
  title = {{NeVAE: A Deep Generative Model for Molecular Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3903},
}

@inproceedings{iclr2023_mole_33047,
  title = {{Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10717},
}

@inproceedings{aaai2022_geomgcl_2154,
  title = {{GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04541-geomgcl-geometric-graph-contrastive-learning-for-molecular-property-prediction/},
}

@inproceedings{icml2020_hierarchical_38280,
  title = {{Hierarchical Generation of Molecular Graphs using Structural Motifs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6213},
}

@inproceedings{icml2022_3d_40136,
  title = {{3D Infomax improves GNNs for Molecular Property Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17331},
}

@inproceedings{neurips2019_n_58652,
  title = {{N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14564},
}

@inproceedings{aaai2022_molecular_2424,
  title = {{Molecular Contrastive Learning with Chemical Element Knowledge Graph}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03968-molecular-contrastive-learning-with-chemical-element-knowledge-graph/},
}

@inproceedings{aaai2023_molformer_4181,
  title = {{Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25662},
}

@inproceedings{aaai2021_mimosa_958,
  title = {{MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/00125-mimosa-multi-constraint-molecule-sampling-for-molecule-optimization/},
}

@inproceedings{aaai2021_molgrow_1008,
  title = {{MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08226-molgrow-a-graph-normalizing-flow-for-hierarchical-molecular-generation/},
}

@inproceedings{iclr2019_learning_28750,
  title = {{Learning Multimodal Graph-to-Graph Translation for Molecule Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/719},
}

@inproceedings{neurips2021_motif_62875,
  title = {{Motif-based Graph Self-Supervised Learning for Molecular Property Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28747},
}

@inproceedings{neurips2022_periodic_66057,
  title = {{Periodic Graph Transformers for Crystal Material Property Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54861},
}

@inproceedings{aaai2020_chemically_100260,
  title = {{Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5433},
}

@inproceedings{iclr2025_grnade_83320,
  title = {{gRNAde: Geometric Deep Learning for 3D RNA inverse design}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28493},
}

@inproceedings{aaai2025_bindgpt_8270,
  title = {{BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34804},
}

@inproceedings{iclr2024_learning_34286,
  title = {{Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18789},
}

@inproceedings{aaai2025_mol_9864,
  title = {{MOL-Mamba: Enhancing Molecular Representation with Structural \& Electronic Insights}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32009},
}

@inproceedings{iclr2024_complete_33872,
  title = {{Complete and Efficient Graph Transformers for Crystal Material Property Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19203},
}

@inproceedings{iclr2025_learning_81207,
  title = {{Learning Molecular Representation in a Cell}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30563},
}

@inproceedings{aaai2024_conformal_5468,
  title = {{Conformal Crystal Graph Transformer with Robust Encoding of Periodic Invariance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27781},
}

@inproceedings{aaai2025_bi_8262,
  title = {{Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32013},
}

@inproceedings{aaai2024_salsa_7198,
  title = {{SALSA: Semantically-Aware Latent Space Autoencoder}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29221},
}

@inproceedings{icml2024_triplet_45097,
  title = {{Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33355},
}

@inproceedings{neurips2024_empowering_71439,
  title = {{Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95817},
}

@inproceedings{icml2024_structure_45451,
  title = {{Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33000},
}

@inproceedings{icml2025_position_48507,
  title = {{Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/40105},
}

@inproceedings{icml2025_neural_48529,
  title = {{Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44092},
}

@inproceedings{aaai2025_enhancing_8973,
  title = {{Enhancing Molecular Representation Learning Through the Combination of 3D and 2D Graph Machine Learning (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35289},
}

@inproceedings{aaai2025_ringformer_10564,
  title = {{RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/31991},
}

@inproceedings{aaai2025_robust_10572,
  title = {{Robust Heterogeneous Graph Classification for Molecular Property Prediction with Information Bottleneck}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32045},
}

@inproceedings{neurips2025_dmol_75251,
  title = {{DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=5szmKUMhB7},
}

@inproceedings{iclr2025_pushing_80758,
  title = {{Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling, and Zero-Shot Transfer}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31010},
}

@inproceedings{aaai2026_graph_107762,
  title = {{Graph VQ-Transformer (GVT): Fast and Accurate Molecular Generation via High-Fidelity Discrete Latents}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40114},
}