Read-Across is widely used to fill data-gaps for untested chemicals. We developed Genralised Read-Across (GenRA) as a computational toxicology tool to mimic a human expert’s manual reasoning based on similarity-weighted activity. This repository contains a Python 3 implementation for GenRA, called genra-py, which is based on the scikit-learn estimator. We also describe two potential uses-cases for genra-py that uses published chemical structure, bioactivity and toxicity data.
pip install genra
or try our Docker image from on dockerhub at [https://hub.docker.com/r/patlewig/genra-py]
The image contains the scipy Jupyter notebook, RDKit and a pip installable version of genra-py (https://github.com/i-shah/genra-py/).
In a terminal type:
docker run --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes --user $(id -u):$(id -g) --group-add users -v "$PWD":/home/jovyan patlewig/genra-py
Copy/paste the resulting url link into a new browser window. This should start the Jupyter lab session from your current working directory.
To avoid typing the long argument in the terminal, using docker-compose is an alternative means of running the container.
Type docker-compose -f genra-docker-compose.yml up
To stop the container simply type:
docker-compose -f genra-docker-compose.yml down
Running the notebooks in this repository requires Python 3, Anaconda, Jupter and some additional configuration.
- Install Python 3, anaconda/conda and Jupyter Lab
- Clone this repo:
git clone https://github.com/i-shah/genra-py.git - Go into genra-py directory and create genra-py conda environment:
make -n create_environment - Activate conda environment:
conda activate genra-py - Add this conda environment as a kernel to jupyter-lab:
ipython kernel install --user --name=genra-py - Copy the notebooks/dotenv file to notebooks/.env and edit the environemnt variables (replace path_to_top with the correct directory name):
TOP=path_to_top/genra-py SRC=path_to_top/genra-py/src DAT=path_to_top/genra-py/data FIG=path_to_top/genra-py/figs
Further details are provided in the notebooks/manual directory.
See https://github.com/patlewig/UNC_Rax and run the example using the Binder https://mybinder.org/
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data <- Data from public domain sources.
│ └─ shah-2016 <- Data from https://doi.org/10.1016/j.yrtph.2016.05.008
│ └─ helman-2019 <- Data from https://doi.org/10.1016/j.yrtph.2016.05.008
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├── notebooks <- Jupyter notebooks
| |
| ├─dotenv <- copy this to ".env" and edit this file
| ├─app-note <- use-cases described in manuscript
| └─manual <- user-manual as a jupyter notebook
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── genra-py.yml <- The spec for creating a conda environment.
| conda env create -f condaenv.yml
├── dist <- Source and Wheel Distributions
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└── genra <- Source code
├─chm <- Chemical structure processing
├─rax <- Read Across prediction
└─utl <- Utilities