| Dataset | # Molecules | # Tasks |
|---|---|---|
| BBBP | 2039 | 1 |
| Tox21 | 7831 | 12 |
| ClinTox | 1478 | 2 |
| HIV | 41127 | 1 |
| BACE | 1513 | 1 |
| SIDER | 1427 | 27 |
| MUV | 93087 | 17 |
| ToxCast | 8575 | 617 |
| Dataset | # Molecules | # Tasks |
|---|---|---|
| ESOL | 1128 | 1 |
| FreeSolv | 642 | 1 |
| Lipophilicity | 4200 | 1 |
| QM7 | 6830 | 1 |
| QM8 | 21786 | 12 |
| QM9 | 133885 | 8 |
[OGB-LSC] OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs (arXiv, 2021) [Paper][Website][PCQM4Mv2]
[GEOM] GEOM, energy-annotated molecular conformations for property prediction and molecular generation (Scientific Data, 2022) [Paper]
[MoleculeNet] MoleculeNet: a benchmark for molecular machine learning (Chemical science, 2018) [Paper]
Applications of deep learning in molecule generation and molecular property prediction (Accounts of chemical research, 2020) [Paper]
Deep learning methods for molecular representation and property prediction (Drug Discovery Today, 2022) [Paper]
[TF_Robust] Massively Multitask Networks for Drug Discovery (arXiv, 2015) [Paper]
[X-MOL] X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis (Science Bulletin, 2022) [Paper] [Code]
[ChemBERTa] ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction (NeurIPS 2020 workshop) [Paper] [Code]
[AGBT] Algebraic graph-assisted bidirectional transformers for molecular property prediction (Nature Communications 2021) [Paper] [Code]
[SMILES Transformer] SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery (arXiv 2019) [Paper] [Code]
[MolCLR] Molecular Contrastive Learning of Representations via Graph Neural Networks (Nature Machine Intelligence, 2022) [Paper][Code]
[GEM] Geometry-enhanced molecular representation learning for property prediction (Nature Machine Intelligence, 2022) [Paper][Code]
[3D Infomax] 3D Infomax improves GNNs for Molecular Property Prediction (ICML 2022) [Paper][Code]
[GraphMVP] Pre-training Molecular Graph Representation with 3D Geometry (ICLR 2022) [Paper] [Code]
[Graphormer] Do Transformers Really Perform Badly for Graph Representation? (NeurIPS 2021) [Paper] [Code]
[MGSSL] Motif-based Graph Self-Supervised Learning for Molecular Property Prediction (NeurIPS 2021) [Paper] [Code]
[MG-BERT] MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction (Briefings in Bioinformatics 2021) [Paper] [Code]
[GROVER] Self-Supervised Graph Transformer on Large-Scale Molecular Data (NeurIPS 2020) [Paper] [Code]
[ATMOL] Attention-wise masked graph contrastive learning for predicting molecular property (BIB 2022) [Paper] [Code] [Chinese blog]
[ImageMol] Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework (Nature Machine Intelligence, 2022) [Paper] [Code] [Chinese blog]
[DMP] Dual-view Molecule Pre-training (arXiv 2021) [Paper] [Code]
[MM-Deacon] Multilingual Molecular Representation Learning via Contrastive Pre-training (ACL 2022) [Paper] [Chinese blog]
Unified 2D and 3D Pre-Training of Molecular Representations (KDD 2022) [Paper] [Code] [Chinese blog]