HybridFragmentTokenization

Installation

• git clone https://github.com/Pixelatory/HybridFragmentTokenization
• download finetuned weights and logs from https://brocku-my.sharepoint.com/:f:/g/personal/na16dg_brocku_ca/Ei6Dk849_FZInrAEXLiYxDUByV0QbG9eeVG0g1CYihDcNQ?e=RxYh4P
• place folders inside HybridFragmentTokenization/
• extract allmolgen_pretrain_data_100maxlen_FIXEDCOLS.tar.xz for pre-training data

Then, to setup conda environment:

• conda env create -f environment.yml

OR

• conda create -n MTL-BERT numpy==1.21.5 pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 rdkit -c rdkit -c pytorch -c nvidia
• pip install tqdm scikit-learn

Inference

Finetuned weights are provided in the installation steps. To perform inference, view the example provided at the base of inference.py

Reference

Aksamit, N., Tchagang, A., Li, Y. et al. Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery. BMC Bioinformatics 25, 255 (2024). https://doi.org/10.1186/s12859-024-05861-z