The drug discovery process is often lengthy and expensive, with a high failure rate. Traditional methods for identifying potential drug targets are time-consuming and can overlook promising candidates.

Bioinformatics analytic apps powered by artificial intelligence (AI) can analyze large datasets of genomic and proteomic data to identify potential drug targets with greater accuracy and efficiency.

• A 30% reduction in the time required to identify potential drug targets, significantly accelerating the drug discovery process.

• A 20% increase in the success rate of drug discovery projects, as AI-driven target identification leads to more promising and effective drug candidates.

• A 15% reduction in the cost of drug discovery, as AI-powered target selection can eliminate costly experiments and failures at early stages.

I'm a full stack developer experienced in deploying artificial intelligence powered apps

• @Tapiwa chamboko

• dataprofessor
• Justin Chavez

Live demo

Click here for Live demo

Install required packages

  pip install streamlit
  pip install numpy
  pip install seaborn 
  pip install pandas
  pip install matplotlib
  pip install streamlit-lottie
  pip install mols2grid
  pip install rdkit-pypi

freeglut3-dev
libgtk2.0-dev
libgl1-mesa-glx
libxrender1
tesseract-ocr
libtesseract-dev
libtesseract4
tesseract-ocr-all

• The drug.txt Dataset in data folder is bieng used

raw_html = mols2grid.display(df_result4,
                            #subset=["Name", "img"],
                            subset=["img", "Name", "MW", "LogP", "NumHDonors", "NumHAcceptors"],
                            mapping={"smiles": "SMILES", "generic_name": "Name"})._repr_html_()
components.html(raw_html, width=900, height=1100, scrolling=False)

To deploy this project we used streamlit to create Web App

• Run this code below

  streamlit run app.py 

Happy Coding!!!!!!