Calculate NPR values & Generate Heatmap: Difference between revisions
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(base)$ conda create -c rdkit --name npr-py3 rdkit | (base)$ conda create -c rdkit --name npr-py3 rdkit | ||
(base)$ conda activate npr-py3 | (base)$ conda activate npr-py3 | ||
# Install vaex - dataframe library for huge libraries | # Install vaex - dataframe library for huge libraries | ||
(npr-py3)$ conda install -c conda-forge vaex | (npr-py3)$ conda install -c conda-forge vaex | ||
(npr-py3)$ pip install ipython==7.9.0 | |||
=== Run NPR calculation === | === Run NPR calculation === | ||
Your smiles file should be in this format with no header: <smiles> <cid> | Your smiles file should be in this format with no header: <smiles> <cid> | ||
(npr-py3)$ python extra_newprops.py {smiles_file} | (npr-py3)$ python extra_newprops.py {smiles_file} | ||
Notes: | Notes: | ||
- Failed and success molecules are output from this script | |||
- Failed and success molecules are output from this script. | |||
- The calculation maybe slow. It is recommend that you chunk the file and run it on parallel. | - The calculation maybe slow. It is recommend that you chunk the file and run it on parallel. | ||
== Make Heatmap == | == Make Heatmap == | ||
=== Generate h5py binary file === | |||
(npr-py3)$ python py_csv2hdf5.py {output_smiles_file} | |||
This script without output h5py that is then can be read by vaex library (it is useful for read huge library into dataframe) | |||
=== Plot === | |||
# Run Jupyter-Notebook | |||
(npr-py3)$ jupyter-notebook | |||
From jupyter-notebook interface | |||
- Select 'single_plot.ipynb' | |||
- Change the path to h5py file and run the kernel | |||
Latest revision as of 07:49, 5 November 2020
Calculate NPR
Setup Python environment
- Download Anaconda3 installer and install follow the instruction (https://www.anaconda.com/products/individual) - Create anaconda env and install packages
(base)$ conda create -c rdkit --name npr-py3 rdkit (base)$ conda activate npr-py3 # Install vaex - dataframe library for huge libraries (npr-py3)$ conda install -c conda-forge vaex (npr-py3)$ pip install ipython==7.9.0
Run NPR calculation
Your smiles file should be in this format with no header: <smiles> <cid>
(npr-py3)$ python extra_newprops.py {smiles_file}
Notes:
- Failed and success molecules are output from this script.
- The calculation maybe slow. It is recommend that you chunk the file and run it on parallel.
Make Heatmap
Generate h5py binary file
(npr-py3)$ python py_csv2hdf5.py {output_smiles_file}
This script without output h5py that is then can be read by vaex library (it is useful for read huge library into dataframe)
Plot
# Run Jupyter-Notebook (npr-py3)$ jupyter-notebook
From jupyter-notebook interface
- Select 'single_plot.ipynb'
- Change the path to h5py file and run the kernel