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 Graph Diffusion Transformer for Multi-Conditional Molecular Generation
 ================================================================
 
+## Initial Setup
+
+Please download NASBench201 dataset(NAS-Bench-201-v1_1-096897.pth) from
+https://drive.google.com/file/d/16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_/view
+
+and put it in the `/path/to/repo/graph_dit` folder.
+
+## Running the code
+
+start command:
+``` bash
+python main.py --config-name=config.yaml \
+model.ensure_connected=True \
+dataset.task_name='nasbench201' \
+dataset.guidance_target='regression'
+```
+
+This repository contains the code for the paper "Inverse Molecular Design with Multi-Conditional Diffusion Guidance" by Gang Liu, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
+
+
 Paper: https://arxiv.org/abs/2401.13858
 
-This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
+<!-- This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
 
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       <img src="asset/arch.png" style="width: 45%;" alt="Description of the second image">
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+</div> -->
 
 
 ## Requirements