MMGX: Multiple Molecular Graph eXplainable Discovery

Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction

Usage 💻

1. Install environment

This code was tested in Python 3.8 with PyTorch 1.13 and rdkit 2023.3.2

Using Conda: conda create -f mmgx.yaml
Then, activate the environment conda activate mmgx

2. Prepare dataset

Prepare dataset in dataset/ folder. Dataset should be in .csv format with smiles, label, and splitting columns.
Indicate the column name in dataset/_dataset.csv file.

3. Hyperparameter tuning

[dataset] = name of dataset without .csv extension
[model] = {GAT, GIN, GAT_edge, Benchmark_GCN, Benchmark_GIN, Benchmark_AttentiveFP}
[schema] = {A (for atom graph only), AR_0 (for combination with pooling), R (for reduced graph)}
[reduced] = {functional, junctiontree, pharmacophore}

python3 hyperparameter.py \
-f [dataset] \
-m [model] \
--schema [schema] \
--reduced [reduced_(optional)] \
--mol_embedding 256 \
--batch_normalize \
--fold 5 \
--seed 42

Examples

# Example, for Atom graph only model
python3 hyperparameter.py \
-f bbbp \
-m GIN \
--schema A \
--reduced \
--mol_embedding 256 \
--batch_normalize \
--fold 5 \
--seed 42

# Example, for Functional graph only model
python3 hyperparameter.py \
-f bbbp \
-m GIN \
--schema R \
--reduced functional \
--mol_embedding 256 \
--batch_normalize \
--fold 5 \
--seed 42

# Example, for 2-graph only model (Atom+Functional)
python3 hyperparameter.py \
-f bbbp \
-m GIN \
--schema AR_0 \
--reduced functional \
--mol_embedding 256 \
--batch_normalize \
--fold 5 \
--seed 42

# Example, for 3-graph model (Atom+Functional+Pharmacophore)
python3 hyperparameter.py \
-f bbbp \
-m GIN \
--schema AR_0 \
--reduced functional pharmacophore \
--mol_embedding 256 \
--batch_normalize \
--fold 5 \
--seed 42

4. Train and test the model

(All can be retrieved from hyperparameter tuning)

[dataset] = name of dataset without .csv extension
[model] = {GAT, GIN, GAT_edge, Benchmark_GCN, Benchmark_GIN, Benchmark_AttentiveFP}
[schema] = {A (for atom graph only), AR_0 (for combination with pooling), R (for reduced graph)}
[reduced] = {functional, junctiontree, pharmacophore}
[batch_size] = {batch size}
[number_of_layer] = {number of node embedding layers for Atom graph}
[number_of_layer_reduced] = {number of node embedding layers for reduced graph}
[in_channels] = {number of input features}
[hidden_channels] = {number of hidden features}
[out_channels] = {number of output features}
[number_of_layer_self] = {number of molecule embedding layers for Atom graph}
[number_of_layer_self_reduced] = {number of molecule embedding layers for reduced graph}

python3 main.py \
-f [dataset] \
-m [model] \
--schema [schema] \
--reduced [reduced graph (optional)] \
--mol_embedding 256 \
--batch_normalize \
--fold 5 \
--seed 42 \
--batch_size [batch_size] \
--num_layers [number_of_layer] \
--num_layers_reduced [number_of_layer_reduced] \
--in_channels [in_channels] \
--hidden_channels [hidden_channels] \
--out_channels [out_channels] \
--num_layers_self [number_of_layer_self] \
--num_layers_self_reduced [number_of_layer_self_reduced] \

Citation 📃

Kengkanna A, Ohue M. Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX. Communications Chemistry. (in press)

Kengkanna A, Ohue M. Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction. In Proceedings of The 20th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2023), 8 pages, 2023. doi: 10.1109/CIBCB56990.2023.10264879

eunos-1128 / MMGX