# Generalised Read-Across (GenRA) in Python
[Read-Across](https://www.oecd.org/env/ehs/risk-assessment/groupingofchemicalschemicalcategoriesandread-across.htm) is widely used to fill data-gaps for untested chemicals. We developed [Genralised Read-Across (GenRA)](https://doi.org/10.1016/j.yrtph.2019.104480) 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](https://scikit-learn.org) [estimator](https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html?highlight=estimator#sklearn.base.BaseEstimator). We also describe two potential uses-cases for genra-py that uses published chemical structure, bioactivity and toxicity data.
# Easy starts
`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`
# Alternatives
Running the notebooks in this repository requires Python 3, [Anaconda](https://www.anaconda.com/), [Jupter](https://jupyter.org/) and some additional configuration.
1. Install Python 3, anaconda/conda and Jupyter Lab
2. Clone this repo: \
`git clone https://github.com/i-shah/genra-py.git`
3. Go into genra-py directory and create *genra-py* conda environment: \
`make -n create_environment`
4. Activate conda environment:\
`conda activate genra-py`
5. Add this conda environment as a kernel to jupyter-lab: \
`ipython kernel install --user --name=genra-py`
6. 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/
# Project Organization
├── 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
|
├── 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
|
└── genra <- Source code
├─chm <- Chemical structure processing
├─rax <- Read Across prediction
└─utl <- Utilities
Raw data
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