Create environment
conda env create -f environment.yml
source activate gpmo
1. Preparation before training the model
create folder, download pre-training checkpoint, and pre-process customered data before training.
1.1 create fold for checkpoint of pre-training and finetune and evaluation
cd experiments
mkdir evaluation_transformer #create folder for evaluation
mkdir train_transformer/checkpointpretrain #create folder for pretraining checkpoint
then download the pre-training checkpoint from here(https://drive.google.com/file/d/1X6aTxecoFwkmtMQb2-d8KMXlU2IpAg5Z/view?usp=share_link) and put it into the pretraining checkpoint
mkdir train_transformer/checkpoint #create folder for finetune checkpoint.
1.2 If you want to train the model with customered data If you use the data provide in data/chembl_02, skip this step. Encode property change, build vocabulary, and split data into train, validation, and test. Outputs are saved in the same directory with input data path.
python preprocess_prop.py --input-data-path data/data_name/data.csv
2. Train model
Train the model and save results and logs to experiments/save_directory/; The model from each epoch is saved in
experiments/save_directory/checkpoint/; The training loss, validation loss and validation accuracy are saved in experiments/save_directory/tensorboard/.
python trainper.py --data-path data/chembl_02 --save-directory train_transformer --model-choice transformer transformer
A pre-trained Transformer model can be found here.
3. Generate molecules
Use the model saved at a given epoch (e.g. 60) to generate molecules for the given test filename, and save the results to experiments/save_directory/test_file_name/evaluation_epoch/generated_molecules.csv. The three test sets used in our paper can be found in data/chembl_02/ as below,
- Test-Original ->
data/chembl_02/test.csv
python generate.py --model-choice transformer --data-path baseline_dataset_modof/ --test-file-name test --model-path experiments/train_transformer/checkpoint --save-directory evaluation_transformer --epoch 60
4. Compute properties for generated molecules
Since we build the property prediction model based on the in-house experimental data, we can't make it public. But the computed properties can be found in experiments/evaluation_transformer/test_file_name/evaluation_60/generated_molecules_prop.csv
5.Evaluate the generated molecules in term of satisfying the desirable properties and draw molecules
python evaluate.py --data-path experiments/evaluation_transformer/test/evaluation_60/generated_molecules_prop.csv
6. Matched molecular pair analysis between starting molecules and generated molecules
- Download mmpdb for matched molecular pair generation
- Parse the downloaded mmpdb path (i.e. path/mmpdb/) to --mmpdb-path of mmp_analysis.py
Between starting molecules and all the generated molecules
python mmp_analysis.py --data-path experiments/evaluation_transformer/test/evaluation_130/generated_molecules.csv --train-path data/chembl_02/train_dmo_prop_encoded.csv --mmpdb-path path/mmpdb/
Between starting molecules and all the generated molecules with desirable properties
python mmp_analysis.py --data-path experiments/evaluation_transformer/test/evaluation_130/generated_molecules_statistics.csv --train-path data/chembl_02/train.csv --mmpdb-path path/mmpdb/ --only-desirable
The code is copyright 2020 by Jiazhen He and distributed under the Apache-2.0 license. See LICENSE for details.