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.

1. AvLiLuMoVe.sdf - Manually combined literature pK_a data^[3]
2. chembl25.sdf - Experimental pK_a data extracted from ChEMBL25^[4]
3. datawarrior.sdf - pK_a data shipped with DataWarrior^[5]
4. combined_training_datasets_unique.sdf - Preprocessed and combined data from datasets (2) and (3), used as training dataset
5. AvLiLuMoVe_cleaned_mono_unique_notraindata.sdf - Preprocessed data from dataset (1), used as external testset
6. novartis_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