Update README.md

Author: drigoni

README.md

@@ -1,8 +1,72 @@
-# Conditional CGVAE
+# Conditional Constrained Graph Variational Autoencoders (CCGVAE) for Molecule Design
+This repository contains the code used to generate the results reported in the paper: [Conditional Constrained Graph Variational Autoencoders for Molecule Design](https://arxiv.org/abs/2009.00725).
+
+```
+@article{rigoni2020conditional,
+  title={Conditional Constrained Graph Variational Autoencoders for Molecule Design},
+  author={Rigoni, Davide and Navarin, Nicol{\`o} and Sperduti, Alessandro},
+  journal={arXiv preprint arXiv:2009.00725},
+  year={2020}
+}
+```
+
 All the files related to the CCGVAE model will be uploaded soon.
 
+# Dependencies
+This project uses the `conda` environment.
+In the `root` folder you can find, for each model, the `.yml` file for the configuration of the `conda` environment and also the `.txt` files for the `pip` environment. 
+Note that some versions of the dependencies can generate problems in the configuration of the environment. For this reason, although the `setup.bash` file is present for the configuration of each project, it is better to configure them manually.
+
+# Structure
+The project is structured as follows: 
+* `data`: contains the code to execute to make the dataset;
+* `results`: contains the checkpoints and the results;
+* `model`: contains the code about the model;
+* `utils`: contains all the utility code.
+
+# Usage
+### Data Download
+First you need to download the necessary files and configuring the environment by running the following commands:
+```bash
+sh setup.bash install
+conda activate givae
+```
+
+### Data Pre-processing
+In order to make de datasets type the following commands:
+```bash
+cd data
+python make_dataset.py --dataset [dataset]
+```
+Where _dataset_ can be:
+* qm9
+* zinc
+
+
+### Model Training
+In order to train the model use:
+```bash
+python CCGVAE.py --dataset [dataset] --config '{"generation":0, "log_dir":"./results", "use_mask":false}'
+```
+
+### Model Test
+In order to generate new molecules:
+```bash
+python CCGVAE.py --dataset [dataset] --restore results/[checkpoint].pickle --config '{"generation":1, "log_dir":"./results", "use_mask":false}'
+```
+
+While, in order to reconstruct the molecules:
+```bash
+python CCGVAE.py --dataset [dataset] --restore results/[checkpoint].pickle --config '{"generation":2, "log_dir":"./results", "use_mask":true}'
+```
+
+In order to analyze the results, we used the following environmet: [ComparisonsDGM](https://github.com/drigoni/ComparisonsDGM).
+
 # Information
 For any questions and comments, contact [Davide Rigoni](mailto:[email protected]).
 
+**NOTE:** Some functions are extracted from the following source [code](https://github.com/microsoft/constrained-graph-variational-autoencoder).
+
 # Licenze
 MIT
+