update readme

Author: liugangcode

README.md

@@ -17,13 +17,7 @@
 
 ---
 
-`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:
-
-1. **Predictive Models**: Done: GREA, SGIR, IRM, GIN/GCN w/ virtual, DIR. SMILES-based LSTM/Transformers. TODO more
-2. **Generative Models**: Done: Graph DiT, GraphGA, DiGress, GDS, MolGPT TODO: more
-3. **Representation Models**: Done: MoAMa, AttrMasking, ContextPred, EdgePred. Many pretrained models from HF. TODO: checkpoints, more 
-
-see the [List of Supported Models](#list-of-supported-models) section.
+`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: **Predictive Models**, **Generative Models**, and **Representation Models**. See the [List of Supported Models](#list-of-supported-models) section for all available models.
 
 > **Note**: This project is in active development, and features may change.
 
@@ -198,6 +192,12 @@ predictions = model.predict(smiles_list)
 
 See the structure of `torch_molecule` with the command `tree -L 2 torch_molecule -I '__pycache__|*.pyc|*.pyo|.git|old*'`
 
+## Plan
+
+1. **Predictive Models**: Done: GREA, SGIR, IRM, GIN/GCN w/ virtual, DIR. SMILES-based LSTM/Transformers. TODO more
+2. **Generative Models**: Done: Graph DiT, GraphGA, DiGress, GDS, MolGPT TODO: more
+3. **Representation Models**: Done: MoAMa, AttrMasking, ContextPred, EdgePred. Many pretrained models from HF. TODO: checkpoints, more 
+
 ## Acknowledgements
 
 This project is under active development, and some features may change over time.