abhiTronix / BBAR

Official Github for "Molecular generative model via retrosynthetically prepared chemical building block assembly" (Advanced Science)

Molecular generative model via retrosynthetically prepared chemical building block assembly

Advanced Science [Paper] [arXiv]

Official github of Molecular generative model via retrosynthetically prepared chemical building block assembly by Seonghwan Seo*, Jaechang Lim, Woo Youn Kim. (Advanced Science)

This repository is improved version(BBARv2) of jaechang-hits/BBAR-pytorch which contains codes and model weights to reproduce the results in paper. You can find the updated architectures at architecture.

If you have any problems or need help with the code, please add an issue or contact [e-mail].

Citation

@article{seo2023bbar,
  title = {Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly},
  author = {Seo, Seonghwan and Lim, Jaechang and Kim, Woo Youn},
  journal = {Advanced Science},
  volume = {10},
  number = {8},
  pages = {2206674},
  doi = {https://doi.org/10.1002/advs.202206674},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/advs.202206674},
}

Table of Contents

• Environment
• Data
  • Dataset Structure
  • Prepare Your Own Dataset
• Model Training
  • Preprocess
  • Training
• Generation

Environment

• python=3.9
• PyTorch=1.12
• Tensorboard=2.11.0
• PyTorch Geometric=2.1.0
• Pandas=1.5.1
• RDKit=2022.3.5
• Parmap=1.6.0

Data

Dataset Structure

Data Directory Structure

Move to data/ directory. Initially, the structure of directory data/ is as follows.

├── data/
    ├── ZINC/               (ZINC15 Database)
    │   ├── smiles/
    │   │   ├── train.smi   (https://github.com/wengong-jin/icml18-jtnn/tree/master/data/zinc/train.txt)
    │   │   ├── valid.smi   (https://github.com/wengong-jin/icml18-jtnn/tree/master/data/zinc/valid.txt)
    │   │   └── test.smi    (https://github.com/wengong-jin/icml18-jtnn/tree/master/data/zinc/test.txt)
    │   ├── get_data.py
    │   └── library.csv
    └── 3CL_ZINC/           (Smina calculation result. (ligands: ZINC15, receptor: 7L13))
        ├── data.csv
        ├── split.csv
        └── library.csv     (Same to data/ZINC/library.csv)

• data/ZINC/, data/3CL_ZINC/ : Dataset which used in our paper.

Prepare ZINC15 Dataset

Move to data/ZINC directory, and run python get_data.py. And then, data.csv and split.csv will be created. The dataset for 3CL docking is already prepared.

├── data/
    ├── ZINC/
        ├── smiles/
        ├── get_data.py
        ├── data.csv      new!
        ├── split.csv     new!
        └── library.csv

Prepare Your Own Dataset

For your own dataset, you need to prepare data.csv and split.csv as follows.

• ./data/<OWN-DATA>/data.csv

  SMILES,Property1,Property2,...
  c1ccccc1,10.25,32.21,...
  C1CCCC1,35.1,251.2,...
  ...
  

  • SMILES must be RDKit-readable.
  • If you want to train a single molecule set with different properties, you don't have to configure datasets separately for each property. You need to configure just one dataset file which contains all of property information. For example, ZINC/data.csv contains information about mw, logp, tpsa, qed, and you can train the model with property or properties, e.g. mw, [mw, logp, tpsa].

• ./data/<OWN-DATA>/split.csv

  train,0
  train,1
  ...
  val,125
  ...
  test,163
  ...
  

  • First column is data type (train, val, test), and second column is index of data.csv.

And then, you need to create a building block library. Go to root directory and run ./script/get_library.py.

cd <ROOT-DIR>
python ./script/get_library.py \
  --data_dir ./data/<OWN-DATA> \
  --cpus <N-CPUS>

After this step, the structure of directory data/ is as follows.

├── data/
    ├── <OWN-DATA>/
        ├── data.csv
        ├── split.csv
        └── library.csv     new!

Model Training

The model training requires less than 12 hours with 1 GPU(RTX2080) and 4 CPUs(Intel Xeon Gold 6234).

Preprocess (Optional)

You can skip data processing during train by pre-processing data with ./script/preprocess.py.

cd <ROOT-DIR>
python ./script/preprocess.py \
  --data_dir ./data/<DATA-DIR> \
  --cpus <N-CPUS>

After preprocessing step, the structure of directory data/ is as follows. data.csv, split.csv and library.csv are required, and data.pkl is optional.

├── data/
    ├── <DATA-DIR>/
        ├── data.csv
        ├── data.pkl      new!
        ├── split.csv
        └── library.csv

Training

cd <ROOT-DIR>
python ./script/train.py -h

Training Script Format Example

Our training script reads model config files ./config/model.yaml. You can change model size by modifying or creating new config files. You can find another arguments through running with -h flag.

python ./script/train.py \
    --name <exp-name> \
    --exp_dir <exp-dir-name> \          # default: ./result/
    --property <property1> <property2> ... \
    --data_dir <DATA-DIR> \             # default: ./data/ZINC/
    --model_config <model-config-path>  # default: ./config/model.yaml

Example running script

python ./script/train.py \
    --name 'logp-tpsa' \
    --exp_dir ./result/ZINC/ \
    --data_dir ./data/ZINC/ \
    --property logp tpsa

python ./script/train.py \
    --name '3cl_affinity' \
    --exp_dir ./result/3cl_affinity/ \
    --data_dir ./data/3CL_ZINC/ \
    --property affinity

Generation

The model generates 20 to 30 molecules per 1 second with 1 CPU(Intel Xeon E5-2667 v4).

Download Pretrained Models.

# Download Weights of pretrained models. (mw, logp, tpsa, qed, 3cl-affinity)
# Path: ./test/pretrained_model/
cd <ROOT-DIR>
sh ./download-weights.sh

Generation

cd <ROOT-DIR>
python ./script/sample.py -h

Example running script.

# Output directory path
mkdir ./result_sample

# Scaffold-based generation. => use `-s` or `--scaffold`
python sample.py \
    -g ./test/generation_config/tpsa.yaml \
    -s "c1ccccc1" \
    --num_samples 100 \
    --logp 6 \
    -o ./result_sample/logp\=6.smi

# Scaffold-based generation. (From File) => use `-S` or `--scaffold_path`
python sample.py \
    --generator_config ./test/generation_config/mw.yaml \
    --scaffold_path ./test/start_scaffolds.smi \
    --num_samples 100 \
    --mw 300 \
    --o ./result_sample/mw\=300.smi \
    --seed 0 -q

Generator config (Yaml)

• generator config format (./config/generator.yaml)

  # If library_builtin_model_path is not null, generator save or load library-builtin model.
  # The library-builtin model contains model parameters and library information.
  # 	(library information: SMILES and latent vector of building block)
  # During configuration process of generator, model vectorizes all building blocks in library.
  # This process requires about 30 seconds. With library-builtin model, this process is skipped.
  # When the file `library_builtin_model_path` exists, upper two parameters (`model_path`, `library_path`) are not needed.
  model_path: <MODEL_PATH>
  library_path: <LIBRARY_PATH>
  library_builtin_model_path: <LIBRARY_BUILTIN_MODEL_PATH>  # optional
  
  # Required
  window_size: 2000
  alpha: 0.75
  max_iteration: 10

• Example (./test/generation_config/logp.yaml)

  model_path: ./test/pretrained_model/logp.tar
  library_path: ./data/ZINC/library.csv
  library_builtin_model_path: ./test/builtin_model/logp.tar
  
  window_size: 2000
  alpha: 0.75
  max_iteration: 10