
## Model description

This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules [WGAN-GP with R-GCN for the generation of small molecular graphs](https://keras.io/examples/generative/wgan-graphs/).

Full credits go to [Alexander Kensert](https://github.com/akensert)

Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)

Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored).

## Intended uses & limitations

Recent implementations of generative models for molecular graphs also include Mol-CycleGAN, GraphVAE, and JT-VAE. For more information on generative adversarial networks, see GAN, WGAN, and WGAN-GP.

## Training and evaluation data

The dataset used in this tutorial is a quantum mechanics dataset (QM9), obtained from MoleculeNet. The QM9 dataset is a good first dataset to work with for generating graphs, as the maximum number of heavy (non-hydrogen) atoms found in a molecule is only nine.

### Training hyperparameters

The following hyperparameters were used during training:
- generator_learning_rate: 5e-04
- discriminator_learning_rate: 5e-04
- train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 50

### Training results

- The results are shown in TensorBoard (Training metrics).

## Model Plot

### View Model Demo
![Model Demo](./demo.png)

<details>
<summary>View Model Plot</summary>

![Model Image](./model.png)

</details>
{"library_name": "keras", "tags": ["unconditional-image-generation"]}
keras tensorboard unconditional-image-generation has_space region:us
TAGS
#keras #tensorboard #unconditional-image-generation #has_space #region-us 


## Model description

This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.

Full credits go to Alexander Kensert

Reproduced by Vu Minh Chien

Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored).

## Intended uses & limitations

Recent implementations of generative models for molecular graphs also include Mol-CycleGAN, GraphVAE, and JT-VAE. For more information on generative adversarial networks, see GAN, WGAN, and WGAN-GP.

## Training and evaluation data

The dataset used in this tutorial is a quantum mechanics dataset (QM9), obtained from MoleculeNet. The QM9 dataset is a good first dataset to work with for generating graphs, as the maximum number of heavy (non-hydrogen) atoms found in a molecule is only nine.

### Training hyperparameters

The following hyperparameters were used during training:
- generator_learning_rate: 5e-04
- discriminator_learning_rate: 5e-04
- train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 50

### Training results

- The results are shown in TensorBoard (Training metrics).

## Model Plot

### View Model Demo
!Model Demo

<details>
<summary>View Model Plot</summary>

!Model Image

</details>
## Model description

This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.

Full credits go to Alexander Kensert

Reproduced by Vu Minh Chien

Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored). ## Intended uses & limitations

Recent implementations of generative models for molecular graphs also include Mol-CycleGAN, GraphVAE, and JT-VAE. For more information on generative adversarial networks, see GAN, WGAN, and WGAN-GP. ## Training and evaluation data

The dataset used in this tutorial is a quantum mechanics dataset (QM9), obtained from MoleculeNet. The QM9 dataset is a good first dataset to work with for generating graphs, as the maximum number of heavy (non-hydrogen) atoms found in a molecule is only nine. ### Training hyperparameters

The following hyperparameters were used during training:
- generator_learning_rate: 5e-04
- discriminator_learning_rate: 5e-04
- train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 50 ### Training results

- The results are shown in TensorBoard (Training metrics). ## Model Plot ### View Model Demo
!Model Demo

<details>
<summary>View Model Plot</summary>

!Model Image

</details>
TAGS
#keras #tensorboard #unconditional-image-generation #has_space #region-us 
 ## Model description

This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.

Full credits go to Alexander Kensert

Reproduced by Vu Minh Chien

Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored). ## Intended uses & limitations

Recent implementations of generative models for molecular graphs also include Mol-CycleGAN, GraphVAE, and JT-VAE. For more information on generative adversarial networks, see GAN, WGAN, and WGAN-GP. ## Training and evaluation data

The dataset used in this tutorial is a quantum mechanics dataset (QM9), obtained from MoleculeNet. The QM9 dataset is a good first dataset to work with for generating graphs, as the maximum number of heavy (non-hydrogen) atoms found in a molecule is only nine. ### Training hyperparameters

The following hyperparameters were used during training:
- generator_learning_rate: 5e-04
- discriminator_learning_rate: 5e-04
- train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 50 ### Training results

- The results are shown in TensorBoard (Training metrics). ## Model Plot ### View Model Demo
!Model Demo

<details>
<summary>View Model Plot</summary>

!Model Image

</details>
passage: TAGS
#keras #tensorboard #unconditional-image-generation #has_space #region-us 
## Model description

This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.

Full credits go to Alexander Kensert

Reproduced by Vu Minh Chien

Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored).## Intended uses & limitations

Recent implementations of generative models for molecular graphs also include Mol-CycleGAN, GraphVAE, and JT-VAE. For more information on generative adversarial networks, see GAN, WGAN, and WGAN-GP.## Training and evaluation data

The dataset used in this tutorial is a quantum mechanics dataset (QM9), obtained from MoleculeNet. The QM9 dataset is a good first dataset to work with for generating graphs, as the maximum number of heavy (non-hydrogen) atoms found in a molecule is only nine.### Training hyperparameters

The following hyperparameters were used during training:
- generator_learning_rate: 5e-04
- discriminator_learning_rate: 5e-04
- train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 50### Training results

- The results are shown in TensorBoard (Training metrics).## Model Plot### View Model Demo
!Model Demo

<details>
<summary>View Model Plot</summary>

!Model Image

</details>