The Python dependencies are:
- Python >= 3.7
- NumPy >= 1.18
- Scikit-Learn >= 0.22
- RDKit >= 2019.09.3
- Pandas >= 0.25
- XGBoost >= 0.90
- JupyterLab >= 1.2
- Matplotlib >= 3.1
- Seaborn >= 0.9
For the data preparation pipeline, ChemAxon Marvin[1] and OpenEye QUACPAC/Tautomers[2] are required. To use the provided prediction model with the included Python script, ChemAxon Marvin[1] is not required.
Of course you also need the code from this repository folder.
First of all you need a working Miniconda/Anaconda installation. You can get Miniconda at https://conda.io/en/latest/miniconda.html.
Now you can create an environment named "ml_pka" with all needed dependencies and activate it with:
conda env create -f environment.yml
conda activate ml_pka
You can also create a new environment by yourself and install all dependencies without the environment.yml file:
conda create -n ml_pka python=3.7
conda activate ml_pka
In case of Linux or macOS:
conda install -c defaults -c rdkit -c conda-forge scikit-learn rdkit xgboost jupyterlab matplotlib seaborn
In case of Windows:
conda install -c defaults -c rdkit scikit-learn rdkit jupyterlab matplotlib seaborn
pip install xgboost
To use the data preparation pipeline you have to be in the repository folder and your conda
environment have to be activated. Additionally the Marvin commandline tool cxcalc
and
the QUACPAC commandline tool tautomers
have to be contained in your PATH
variable.
Also the environment variables OE_LICENSE
(containing the path to your OpenEye license
file) and JAVA_HOME
(referring to the Java installation folder, which is needed for
cxcalc
) have to be set.
After preparation you can display a small usage information with bash run_pipeline.sh -h
.
Example call:
bash run_pipeline.sh --train datasets/chembl25.sdf --test datasets/AvLiLuMoVe.sdf
First of all you have to be in the repository folder and your conda environment have
to be activated. To use the prediction tool you have to retrain the machine learning model.
Therefore just call the training script, it will train the 5-fold cross-validated Random
Forest machine learning model using 12 cpu cores. If you want to adjust the number of
cores you can edit the train_model.py
by changing the value of the variable EST_JOBS
.
python train_model.py
To use the prediction tool with the trained model QUACPAC/Tautomers have to be available as it was mentioned in the chapter above.
Now you can call the python script with a SDF file and an output path:
python predict_sdf.py my_test_file.sdf my_output_file.sdf
NOTE: This model was build for monoprotic structures regarding a pH range of 2 to 12. If the model is used with multiprotic structures, the predicted values will probably not be correct.
AvLiLuMoVe.sdf
- Manually combined literature pKa data[3]chembl25.sdf
- Experimental pKa data extracted from ChEMBL25[4]datawarrior.sdf
- pKa data shipped with DataWarrior[5]combined_training_datasets_unique.sdf
- Preprocessed and combined data from datasets (2) and (3), used as training datasetAvLiLuMoVe_cleaned_mono_unique_notraindata.sdf
- Preprocessed data from dataset (1), used as external testsetnovartis_cleaned_mono_unique_notraindata.sdf
- Preprocessed data from an inhouse dataset provided by Novartis[6], used as external testset
Marcel Baltruschat - GitHub, E-Mail
Paul Czodrowski - GitHub, E-Mail
This project is licensed under the MIT License - see the LICENSE.md file for details.
[1] Marvin 20.1.0, 2020, ChemAxon, http://www.chemaxon.com
[2] QUACPAC 2.0.2.2: OpenEye Scientific Software, Santa Fe, NM. http://www.eyesopen.com
[3] Settimo, L., Bellman, K. & Knegtel, R.M.A. Pharm Res (2014) 31: 1082.
https://doi.org/10.1007/s11095-013-1232-z
[4] Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F,
Bellis LJ, Cibrián-Uhalte E, Davies M, Dedman N, Karlsson A, Magariños MP, Overington JP,
Papadatos G, Smit I, Leach AR. (2017) 'The ChEMBL database in 2017.' Nucleic Acids Res.,
45(D1) D945-D954.
[5] Thomas Sander, Joel Freyss, Modest von Korff, Christian Rufener. DataWarrior: An Open-Source
Program For Chemistry Aware Data Visualization And Analysis. J Chem Inf Model
2015, 55, 460-473, doi 10.1021/ci500588j
[6] Richard A. Lewis, Stephane Rodde, Novartis Pharma AG, Basel, Switzerland