molecularai/deep-molecular-optimization

Molecular optimization by capturing chemist’s intuition using the Seq2Seq with attention and the Transformer

Please note: this repository is no longer being maintained.

Molecular Optimization by Capturing Chemist's Intuition Using Deep Neural Networks

Description

Implementation of the Seq2Seq with attention and the Transformer used in Molecular Optimization by Capturing Chemist's Intuition Using Deep Neural Networks. Given a molecule and desirable property changes, the goal is to generate molecules with desirable property changes. This problem can be viewed as a machine translation problem in natural language processing. Property changes are incorporated into input together with SMILES.

Usage

Create environment

conda env create -f environment.yml
source activate molopt

1. Preprocess data

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.py --input-data-path data/chembl_02/mmp_prop.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 train.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
• Test-Molecule -> data/chembl_02/test_not_in_train.csv
• Test-Property -> data/chembl_02/test_unseen_L-1_S01_C10_range.csv

python generate.py --model-choice transformer --data-path data/chembl_02 --test-file-name test --model-path experiments/train_transformer/checkpoint --save-directory evaluation_transformer --epoch 60
python generate.py --model-choice transformer --data-path data/chembl_02 --test-file-name test_not_in_train --model-path experiments/train_transformer/checkpoint --save-directory evaluation_transformer --epoch 60
python generate.py --model-choice transformer --data-path data/chembl_02 --test-file-name test_unseen_L-1_S01_C10_range --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
python evaluate.py --data-path experiments/evaluation_transformer/test_not_in_train/evaluation_60/generated_molecules_prop.csv
python evaluate.py --data-path experiments/evaluation_transformer/test_unseen_L-1_S01_C10_range/evaluation_60/generated_molecules_prop.csv --range-evaluation lower

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_60/generated_molecules_prop.csv --train-path data/chembl_02/train.csv --mmpdb-path path/mmpdb/
python mmp_analysis.py --data-path experiments/evaluation_transformer/test_not_in_train/evaluation_60/generated_molecules_prop.csv --train-path data/chembl_02/train.csv --mmpdb-path path/mmpdb/
python mmp_analysis.py --data-path experiments/evaluation_transformer/test_unseen_L-1_S01_C10_range/evaluation_60/generated_molecules_prop.csv --train-path data/chembl_02/train.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_60/generated_molecules_prop_statistics.csv --train-path data/chembl_02/train.csv --mmpdb-path path/mmpdb/ --only-desirable
python mmp_analysis.py --data-path experiments/evaluation_transformer/test_not_in_train/evaluation_60/generated_molecules_prop_statistics.csv --train-path data/chembl_02/train.csv --mmpdb-path path/mmpdb/ --only-desirable
python mmp_analysis.py --data-path experiments/evaluation_transformer/test_unseen_L-1_S01_C10_range/evaluation_60/generated_molecules_prop_statistics.csv --train-path data/chembl_02/train.csv --mmpdb-path path/mmpdb/ --only-desirable

License

The code is copyright 2020 by Jiazhen He and distributed under the Apache-2.0 license. See LICENSE for details.