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In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs.

Python 0.27% Jupyter Notebook 99.73%

mediprobe-drug-discovery's Introduction

MediProbe- A Drug Discovery Web App

This is a Bioinformatics Project from Scratch - Drug Discovery (Deployed Model as Web 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

conda create -n bioactivity python=3.7.9

Secondly, we will login to the bioactivity environement

conda activate bioactivity

Install prerequisite libraries

Download requirements.txt file

wget https://github.com/theshredbox/MediProbe-Drug-Discovery/blob/main/requirements.txt

Pip install libraries

pip install -r requirements.txt

Download and unzip contents from GitHub repo

Download and unzip contents from https://github.com/theshredbox/MediProbe-Drug-Discovery

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 bioactivity_prediction_app.ipynb. Upon successfully running all code cells, a pickled model called acetylcholinesterase_model.pkl will be generated.

Launch the app

streamlit run app.py

mediprobe-drug-discovery's People

Contributors

theshredbox avatar

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