Code for the paper: Graph Neural Networks for Predicting Chemical Reaction Performance

Installation

1. First install Anaconda.
2. Create a conda environment with

conda create --name rxntorch python=3.6

3. Then, activate the new conda environment with

conda activate rxntorch

4. Install RDKit

conda install -c rdkit rdkit 

5. Installing PyTorch with a CUDA enabled version

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

6. Install scikit-learn with

conda install scikit-learn

Finally, clone this repository to your local machine.

Getting Started

1. Run:

 pip install requirements

2. Download the data from the below google drive links and put it in: ./data/<data_name>/raw/

Suzuki data (su)

Doyle data (dy)

AstraZeneca data (az)

3. Prepare the domain features (chemical properties) by running

python 01_prepare_data.py --dataset_name <data_name> --use_rdkit_feats <rdkit or no_rdkit> --test_ratio <test_ratio>

4. Depending on the type of the features you're using (with rdkit or no rdkit) you can do feature selection using:

python 02_train_rf.py --dataset_name <data_name>

If you're not using rdkit, you don't have to do feature selection because the feature set is not too large.

5. To train the model, run:

python train_yield.py

Important arguments:

-p: Dataset path
-dn: Dataset name (su,dy,az)
-op: Output path
-mv: Model version
--split_set_num: Which split set to use. This is generated by running 01_prepare_data.py
--use_domain: Use chemical features or not. Options: (rdkit, no_rdkit, no_domain)

--epochs: Number of epochs
--seed: Random seed

--layers: Number of layes
--hidden: Hidden size for all layers
--lr_decay: Learning rate decay
--batch_size: Size of mini-batch
--dropout_rate: Droput rate

6. To generate model predictions and visualize the activations of the GNN, run:

05_load_model.ipynb

If using chemical features (domain features) you need the json file containg the features. Otherwise, you can just use smiles strings.

Not super cleaned yet, but this just generates some plots:

7. To plot the training curves and get the avg perfroamnce, run:

04_plots.ipynb