update readme

Author: liugangcode

README.md

@@ -19,10 +19,52 @@
 
 `torch-molecule` is a package under active development that facilitates molecular discovery through deep learning, featuring a user-friendly, `sklearn`-style interface. It includes model checkpoints for efficient deployment and benchmarking across a range of molecular tasks. Currently, the package focuses on three main components:
 
+Here are the three Markdown tables, each showing only the models you have already supported for each category:
+
+Below are three Markdown tables that list only the supported models. Each table includes a column for the model name and a column for its reference. Since specific references were not provided, a placeholder "[Reference unknown]" is used.
+
 1. **Predictive Models**: Done: GREA, SGIR, IRM, GIN/GCN w/ virtual, DIR. TODO: SMILES-based LSTM/Transformers, more
 2. **Generative Models**: Done: Graph DiT, GraphGA, DiGress. TODO:, GDSS, more
 3. **Representation Models**: Done: MoAMa, AttrMasking, ContextPred, EdgePred. Many pretrained models from HF. TODO: checkpoints, more 
 
+### Predictive Models
+
+| Model                | Reference           |
+|----------------------|---------------------|
+| SGIR                 | [Semi-Supervised Graph Imbalanced Regression. KDD 2023](https://dl.acm.org/doi/10.1145/3580305.3599497) |
+| GREA                | [Graph Rationalization with Environment-based Augmentations. KDD 2022](https://dl.acm.org/doi/abs/10.1145/3534678.3539347) |
+| DIR                  | [Discovering Invariant Rationales for Graph Neural Networks. ICLR 2022](https://arxiv.org/abs/2201.12872) |
+| SSR                  | [SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. NeurIPS 2022](https://arxiv.org/abs/2206.07096) |
+| IRM                  | [Invariant Risk Minimization](https://arxiv.org/abs/1907.02893) |
+| RPGNN                | [Relational Pooling for Graph Representations. ICLR 2019](https://arxiv.org/abs/1903.02541) |
+| GNNs                 | [Graph Convolutional Networks. ICLR 2017](https://arxiv.org/abs/1609.02907) and [Graph Isomorphism Network. ICLR 2019](https://arxiv.org/abs/1810.00826) |
+| Transformer (SMILES) | [Attention is All You Need. NeurIPS 2017](https://arxiv.org/abs/1706.03762) based on SMILES strings |
+| LSTM (SMILES)        | [Long short-term memory (Neural Computation 1997)](https://ieeexplore.ieee.org/abstract/document/6795963) based on SMILES strings |
+
+### Generative Models
+
+| Model      | Reference           |
+|------------|---------------------|
+| Graph DiT  | [Graph Diffusion Transformers for Multi-Conditional Molecular Generation. NeurIPS 2024](https://openreview.net/forum?id=cfrDLD1wfO) |
+| DiGress    | [DiGress: Discrete Denoising Diffusion for Graph Generation. ICLR 2023](https://openreview.net/forum?id=UaAD-Nu86WX) |
+| GDSS       | [Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations. ICML 2022](https://proceedings.mlr.press/v162/jo22a/jo22a.pdf) |
+| MolGPT     | [MolGPT: Molecular Generation Using a Transformer-Decoder Model. Journal of Chemical Information and Modeling 2021](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00600) |
+| GraphGA    | [A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules. Journal of Chemical Information and Computer Sciences 2004](https://pubs.acs.org/doi/10.1021/ci034290p) |
+
+### Representation Models
+
+### Pretraining Methods
+
+| Model        | Reference           |
+|--------------|---------------------|
+| MoAMa        | [Motif-aware Attribute Masking for Molecular Graph Pre-training. LoG 2024](https://arxiv.org/abs/2309.04589) |
+| AttrMasking  | [Strategies for Pre-training Graph Neural Networks. ICLR 2020](https://arxiv.org/abs/1905.12265) |
+| ContextPred  | [Strategies for Pre-training Graph Neural Networks. ICLR 2020](https://arxiv.org/abs/1905.12265) |
+| EdgePred     | [Strategies for Pre-training Graph Neural Networks. ICLR 2020](https://arxiv.org/abs/1905.12265) |
+| InfoGraph    | [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. ICLR 2020](https://arxiv.org/abs/1908.01000) |
+| Supervised   | Supervised pretraining |
+| Pretrained   | More than ten pretrained models from [Hugging Face](https://huggingface.co) |
+
 > **Note**: This project is in active development, and features may change.
 
 ## Installation