Introducing DeepDrugDomain: A cutting-edge toolkit for Drug-Target Interaction & Affinity Prediction. Streamlined preprocessing, advanced modeling capabilities, and more - all in one comprehensive library. Revolutionize your DTI and DTA research with #DeepDrugDomain! Check it out now: Github #Bioinformatics #MachineLearning #DrugDiscovery
✨ Coming Soon: We are thrilled to announce that a new project, the DeepDrugDomain Package, is in the works! While this current repository has served us well, we're gearing up for an exciting transition to a more advanced and feature-rich package.
The DeepDrugDomain Package will offer enhanced capabilities for drug-target interaction and affinity prediction, integrating the latest advancements in the field. Although the repository is not public yet, we're just putting on the finishing touches.
Keep an eye on this page for future updates and the big reveal!
Thank you for your interest and support!
This is The repository for supporting matterial of FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation (Neurips AI4D3 Workshop 2023).
A suitable conda environment can be created:
## Create Conda env
conda create -n fragx python=3.9
conda activate fragx
## Installations
pip install numpy==1.26.4, scikit-learn, dgllife, rdkit-pypi, tensorflow, igraph, biopython
# Torch requirements: >2.0, <2.4
pip install torch==2.2.0
# dgl should be CUDA-supported
pip install dgl -f https://data.dgl.ai/wheels/torch-2.2/cu121/repo.html
pip install deepchem==2.5.0
If you find this repo to be useful or if you use Datasets with 3D structures included, please cite our papers. Thank you.
FragXsiteDTI:
@article{yalabadi2023fragxsitedti,
title={FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation},
author={Yalabadi, Ali Khodabandeh and Yazdani-Jahromi, Mehdi and Yousefi, Niloofar and Tayebi, Aida and Abdidizaji, Sina and Garibay, Ozlem Ozmen},
journal={arXiv preprint arXiv:2311.02326},
year={2023}
}
AttentionSiteDTI:
@article{yazdani2022attentionsitedti,
title={AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification},
author={Yazdani-Jahromi, Mehdi and Yousefi, Niloofar and Tayebi, Aida and Kolanthai, Elayaraja and Neal, Craig J and Seal, Sudipta and Garibay, Ozlem Ozmen},
journal={Briefings in Bioinformatics},
volume={23},
number={4},
pages={bbac272},
year={2022},
publisher={Oxford University Press}
}