synformer 0.1.1

SynFormer: Generative Model for Synthesizable Molecule Generation

SynFormer

SynFormer is a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space.

[Paper]

Install

Environment

# Install conda environment
conda env create -f env.yml -n synformer
conda activate synformer

# Install SynFormer package
pip install --no-deps -e .

Building Block Database

We provide preprocessed building block data. You can download it from here and put it in the data directory.

However, the data is derived from Enamine's building block catalog, which are available only upon request. Therefore, you should first request the data from Enamine here and download the US Stock catalog into the data/building_blocks directory. Then run the following script which will check whether you have a copy of the Enamine's catalog and unarchive the preprocessed data for you:

python scripts/unarchive_wizard.py

Trained Models

You can download the trained weights from here and put them in the data/trained_weights directory.

Usage

Bottom-Up Synthesis Planning

You can create a list of SMILES strings in CSV format (example: data/example.csv) and run the following command to project them into the synthesizable chemical space.

python sample.py \
    --model-path data/trained_weights/original_default.ckpt \
    --input data/example.csv \
    --output results/example.csv

Reference

If you find our code/model useful, please cite our papers:

@article{gao2024synformer,
  title={Generative artificial intelligence for navigating synthesizable chemical space},
  author={Wenhao Gao and Shitong Luo and Connor W. Coley},
  year={2024}
}

@inproceedings{luo2024chemprojector,
  title={Projecting Molecules into Synthesizable Chemical Spaces},
  author={Shitong Luo and Wenhao Gao and Zuofan Wu and Jian Peng and Connor W. Coley and Jianzhu Ma},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024}
}