init

README.md

@@ -0,0 +1,86 @@
+Inverse Molecular Design with Multi-Conditional Diffusion Guidance
+================================================================
+
+Paper: https://arxiv.org/abs/2401.13858
+
+This is the code for MCD: a Multi-Conditional Diffusion Model for inverse small molecule and polymer designs and generations. The denoising model architecture in `mcd/models` looks like:
+
+<div style="display: flex;" markdown="1">
+      <img src="asset/reverse.png" style="width: 50%;" alt="Description of the first image">
+      <img src="asset/arch.png" style="width: 50%;" alt="Description of the second image">
+</div>
+
+
+## Requirements
+All dependencies are specified in the `requirements.txt` file.
+
+This code was developed and tested with Python 3.9.16, PyTorch 2.0.0, and PyG 2.3.0, pytorch-lightning 2.0.1.
+
+For molecular generation evaluation, we should first install rdkit: 
+
+Then `fcd_torch`: `pip install fcd_torch` (https://github.com/insilicomedicine/fcd_torch).
+
+And `mini_moses` package: `pip install git+https://github.com/igor-krawczuk/mini-moses` (https://github.com/igor-krawczuk/mini-moses),
+
+## Usage
+
+We could train the model on an A6000 GPU card. Here is an example to running the code for polymer graphs:
+
+```
+python main.py --config-name=config.yaml \
+                model.ensure_connected=True \
+                dataset.task_name='O2-N2-CO2' \
+                dataset.guidance_target='O2-N2-CO2'
+```
+All default configurations can be found in `configs/config.yaml`. In this example, we set `model.ensure_connected=True` to ensure that all generated components are retained during graph-to-molecule conversion (see paper Section 3.2).
+
+Other examples for small molecule generation:
+
+```
+python main.py --config-name=config.yaml \
+                dataset.task_name='bace_b' \
+                dataset.guidance_target='Class'
+
+python main.py --config-name=config.yaml \
+                dataset.task_name='bbbp_b' \
+                dataset.guidance_target='p_np'
+
+python main.py --config-name=config.yaml \
+                dataset.task_name='hiv_b' \
+                dataset.guidance_target='HIV_active'
+```
+
+We could generate polymer graphs by conditioning on single gas permeability.
+
+```
+
+python main.py --config-name=config.yaml \
+                dataset.task_name='O2' \
+                dataset.guidance_target='O2'
+
+python main.py --config-name=config.yaml \
+                dataset.task_name='N2' \
+                dataset.guidance_target='N2'
+
+python main.py --config-name=config.yaml \
+                dataset.task_name='CO2' \
+                dataset.guidance_target='CO2'
+```
+
+Feel free to test the code on your own dataset!
+
+## Citation
+
+If you find this repository useful, please cite our paper:
+
+```
+@misc{liu2024inverse,
+      title={Inverse Molecular Design with Multi-Conditional Diffusion Guidance}, 
+      author={Gang Liu and Jiaxin Xu and Tengfei Luo and Meng Jiang},
+      year={2024},
+      eprint={2401.13858},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+