ChemBoost

ChemBoost is a chemical-language based drug - target affinity prediction framework. The models in ChemBoost leverage distributed chemical word vectors to represent chemicals and a combination of sequence-driven and ligand-centric features to represent proteins. Thanks to chemical-language based representations, several ChemBoost models achieve state-of-the-art level prediction performance on BDB and KiBA data sets and a demonstrated robustness to cases where functional protein similarity cannot be inferred from the similarities in the protein sequence.

How to Run

The experiments in ChemBoost is run with the following configuration:

python=3.7.3
numpy=1.16.4
pandas=0.24.2
xgboost=0.90
sklearn=0.21.2
gensim=3.8.0
sentencepiece==0.1.95

To run the experiments from scratch:

• Install the packages listed above. (Using conda is recommended, except sentencepiece which can be installed through pip.)
• Download the datasets from this link and place them under the data folder.
• Then use the following command format under the root directory in order to replicate results for a model:

python run_experiments.py {dataset_name} {model_name} {save_name}

where {dataset_name} is replaced with either kiba or bdb and {model_name} expects the input to be in the models in the paper. See Line 15 in src/utils.py for a full list of acceptable model names. Last, you can set {save_name} any filename of your choice.

Citation

If you use ChemBoost in your study, please cite:

@article{ozccelik2020chemboost,
  title={ChemBoost: A Chemical Language Based Approach for Protein--Ligand Binding Affinity Prediction},
  author={{\"O}z{\c{c}}elik, R{\i}za and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan and Ozkirimli, Elif},
  journal={Molecular Informatics},
  year={2020},
  publisher={Wiley Online Library}
}