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Notebook for standardization of actvity data, nonadditivity analysis and its evaluation.

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Please note: this repository is no longer being maintained.

NonadditivityAnalysis

Notebook for standardization of actvity data, nonadditivity analysis and its evaluation.

A jupyter notebook for:

  1. Cleaning and standardizing ChEMBL activity data
  2. Running nonadditivity analysis based on NAA code published by C. Kramer [1]
  3. Evaluating the nonadditivity results

[1] Kramer C (2019) Nonadditivity Analysis. J Chem Inf Model 59:4034–4042. https://doi.org/10.1021/acs.jcim.9b00631


Requirements

Installation requirements are the same as for the published NAA code:

  • A copy of the RDKit cheminformatics toolkit, available from http://rdkit.org/

  • A running version of mmpdb, a matched molecular pairs database generation and analysis toolkit, available from http://github.com/rdkit/mmpdb

    git clone https://github.com/rdkit/mmpdb.git
    cd mmpdb
    python setup.py install
  • A running version of NAA, nonadditivity analysis code, available from https://github.com/KramerChristian/NonadditivityAnalysis

    git clone https://github.com/KramerChristian/NonadditivityAnalysis.git
    cd NonadditivityAnalysis
    # Add mmpdb path to line 44 of NonadditivityAnalysis.py

In order to run the NAA code directly from jupyter notebook, you need to set an environmental variable. Therefore, on command line, generate the following directories and files:

cd $CONDA_PREFIX
mkdir -p ./etc/conda/activate.d
mkdir -p ./etc/conda/deactivate.d
touch ./etc/conda/activate.d/env_vars.sh
touch ./etc/conda/deactivate.d/env_vars.sh

then edit ./etc/conda/activate.d/env_vars.sh as follows:

#!/bin/sh
export NAA='/path/to/naa/code/'

and edit ./etc/conda/deactivate.d/env_vars.sh as follows:

#!/bin/sh
unset NAA

Apart from this, standard scientific python libraries like scipy and numpy are required as well as seaborn and matplotlib for plot generation in the analysis part.

conda install -c anaconda scipy
conda install -c anaconda numpy
conda install -c conda-forge matplotlib
conda install seaborn

Usage

The jupyter notebook can be run directly with gzipped activity data downloaded from ChEMBL, as an example the activity data for ChEMBL1614027 (ChEMBL Version 27) is included in this package. 'my_path' and 'my_name' has to be adjusted at the beginning of the jupyter notebook to reflect the user's specific path and output name.

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