Skip to content

This a web app built using StreamLit that uses machine learning to predict bioactivity of molecules entered in SMILES format

Notifications You must be signed in to change notification settings

dessygil/BioactivityPredictorPADI4

 
 

Repository files navigation

bioactivity-prediction-app

Reproducing this web app

To recreate this web app on your own computer, do the following.

Create conda environment

Firstly, we will create a conda environment called bioactivity

 python3 -m venv venv

Secondly, we will login to the bioactivity environement

source venv/bin/activate

Install prerequisite libraries

Pip install libraries

pip install -r requirements.txt

Generating the PKL file

The machine learning model used in this web app will firstly have to be generated by successfully running the included Jupyter notebook. Upon successfully running all code cells, a pickled model called PADI4_model.pkl will be generated.

Launch the app

streamlit run app.py

Exploring the process

If you would like to explore the process of choosing the data, cleaning it, processing it and then ultimately choosing a model you can check it out in DesDrugDisco.ipynb

About

This a web app built using StreamLit that uses machine learning to predict bioactivity of molecules entered in SMILES format

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.7%
  • Python 0.3%