Wang-Lin-boop / DeepSA

DeepSA: A Deep-learning Driven Predictor of Compound Synthesis Accessibility

With the continuous development of artificial intelligence technology, more and more deep-generation models are used for molecule generation. However, most new molecules generated by the generation models often face great challenges in terms of synthetic accessibility.

DeepSA is proposed to predict synthesis accessibility of compounds, and has a much higher early enrichment rate in discriminating molecules that are difficult to synthesize. This helps users to select less expensive molecules for synthesis, thus reducing the time for drug discovery and development. You can use DeepSA on a webserver at https://bailab.siais.shanghaitech.edu.cn/deepsa

Requirements

• Python == 3.8.13
• scikit-learn == 1.0.2
• pandas == 1.4.2
• numpy == 1.21.6
• matplotlib == 3.2.2

Dependencies can be installed using the following command:

conda create -n DeepSA python=3.8.13
conda activate DeepSA

pip3 install -U pip
pip3 install -U setuptools wheel
# for cpu version
pip3 install torch==1.12+cpu torchvision==0.13.0+cpu torchtext==0.13.0 -f https://download.pytorch.org/whl/cpu/torch_stable.html
# for gpu version
# pip3 install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchtext==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip3 install autogluon==0.5.2
pip3 install rdkit

Data

The expand training and tes datasets could be easily downloaded at https://drive.google.com/drive/folders/1iup6T3Bqyy-uvpdFyP0Of_WQqn-9l62h?usp=sharing

Usage For Researchers

If you want to train your own model, you can run it from the command line,

running:

    python DeepSA_training.py <dataset.csv/training.csv:test.csv> DeepSA ./data/test_set.list

If you want to use the model we proposed,

running:

    python DeepSA.py <input_data.csv> DeepSA

Online Server

We deployed a pre-trained model on a dedicated server, which is publicly available at https://bailab.siais.shanghaitech.edu.cn/deepsa, to make it easy for biomedical researcher users to utilize DeepSA in their research activity.

Users can upload their SMILES or csv files to the server, and then they can quickly obtain the predicted results.

Citation

If you find this repository useful in your research, please consider citing our paper:

Wang, S., Wang, L., Li, F. et al. DeepSA: a deep-learning driven predictor of compound synthesis accessibility. J Cheminform 15, 103 (2023). https://doi.org/10.1186/s13321-023-00771-3

Contact

If you have any questions, please feel free to contact Shihang Wang (Email: [e-mail]) or Lin Wang (Email: [e-mail]).

Pull requests are highly welcomed!

Acknowledgments

We are grateful for the support from HPC Platform of ShanghaiTech University.
Thank you all for your attention to this work.