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# NAG2G: Node-Aligned Graph-to-Graph Model

Welcome to the NAG2G (Node-Aligned Graph-to-Graph) repository! NAG2G is a state-of-the-art neural network model for retrosynthesis prediction.

## 🔥 Latest Updates

* **🗓️ May 2025** — 🌿 *[`with_stereoisomerism`](https://github.com/your-repo-link/tree/with_stereoisomerism) branch is now publicly available*

> 📝 *This branch was completed in 2023 but wasn’t released until now — opened in response to community interest.*
 >
 >

**New in this branch:**

* 💊 **Enhanced Stereochemistry Support**

* Direct prediction of stereochemical features (e.g., chirality) from model outputs
    * No post-processing required for stereochemical reconstruction
  * ⇄ **Unified Bidirectional Synthesis**

* Single model supports both **retrosynthesis** and **forward synthesis**

* **🗓️ August 2024** — 💻 *Initial codebase released* ([`main`](https://github.com/your-repo-link) branch)

* **🗓️ February 2024** — 🧪 *Paper published in* [JACS Au](https://pubs.acs.org/doi/full/10.1021/jacsau.3c00737)

* **🗓️ September 2023** — 📄 *Preprint available on* [ArXiv](https://arxiv.org/abs/2309.15798)

## Environment Setup

To begin working with NAG2G, you'll need to set up your environment. Below is a step-by-step guide to get you started:

```bash
# Install Uni-Core
git clone https://github.com/dptech-corp/Uni-Core
cd Uni-Core
pip install .
cd -

# Install Unimol plus
cd unimol_plus
pip install .
cd -

# Install additional dependencies
pip install rdchiral transformers tokenizers omegaconf rdkit
```

## Datasets and Pretrained Weights

You can obtain the dataset USPTO-50k and pretrained model weights for USPTO-50k from [the Google Drive](https://drive.google.com/drive/folders/1lZOLRGyZy18EVow7gyxtKWvs_yuwlIE3?usp=sharing):

## Model Validation

To validate the NAG2G model with the provided weights, follow the instructions below:

When using a dataset that does not include reactants, you need to modify the `valid.sh` script. Specifically, add the ` --no_reactant` command in line 95 in the code.

When using your own dataset, please modify the `data_path` in the `valid.sh` script.

```bash
# Execute the validation script with the specified checkpoint file
sh valid.sh path2weight/NAG2G_unimolplus_uspto_50k_20230513-222355/checkpoint_last.pt
```

## Data Preprocessing Instructions

If you need to regenerate the dataset, please refer to the code inside the `data_preprocess` directory.

```bash
cd data_preprocess
python lmdb_preprocess <input_csv> <output_lmdb>
```

Two sample CSV files are provided for reference:
- `sample.csv`: This sample includes given reactants.
- `sample_without_reactants.csv`: This sample does not include given reactants.

----
For any questions or issues, please open an issue on our GitHub repository.

Thank you for your interest in NAG2G!

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