🧬 🧪 Alzheimer-drug-discovery

Hearing the stories of people suffering from alzheimer is always heartbreaking. Our goal is to make machine learning beneficial for everybody.

📜 Definitions

a drug is biological or chemical entity that can modulate the course of a desease state by interacting with its target protein

• biological entity is composed of proteins or peptide. (e.g. antibodies)
• chemical entity is composed of compounds aka molecules. (e.g. small molecules)

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Bioinformatics is a discipline entailing the useof computational approaches to analyze biological data.

• Analyze and compare genes, proteins and genomes.
• Exploring structures and functions of biomolecules (DNA, protein, lipid and carbohydrate).
• explore network biology and letabolic pathways.

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✨ Demo

streamlit-app-2022-03-31-21-03-69.veed.ioX2.mp4

🏗 Project's structure

in this subsection we will emphasise the role each directory plays in thos repository.

• notebooks contains jupyter notebooks for all the hooks a professional model should pass through, from data gathering, EDA, approving or denying hypothesis, model training and models comparison.
• data contains data processed at each step.
• figures contains saved figures that could be useful for scientists writing their report.
• toolkits contains required files to run PaDEL descriptors
• bioactivity-prediction-app here resides the webapp, it summarizes all efforts done in other directories.

🧑‍💻 Usage

• Prerequisites

  Make sure you have:

  • python 3.X installed
  • conda environment by installing anaconda or miniconda (recommended).

• Serializing the model

  The size of the serialized model was relatively too large so you have to generate it by yourself.

  Under bioactivity-prediction-app directory, open the following Jupyter Notebook bioactivity_prediction_model.ipynb and run it with your conda environment.

  You will remark that a new file, bioactivity_prediction_model.ipynb, appears.

• Launching application

  Under bioactivity-prediction-app directory, open the bash terminal and execute the following commands.

  1. create a virtual environment

  python3 -m venv alzheimer-drug-discovery
  

  2. activate the virtual environment

  source alzheimer-drug-discovery/bin/activate 
  

  3. install the projects' dependencies

  pip install -r requirements.txt
  

  4. start the application

  streamlit run app.py
  

Enjoy interacting with your application on your favorite web browser 🎉

🧑‍🔬 Domain knowledge

building domain knowledge is the most fragile part of every data science project. It took me hours of research to get the fundamentals of bioinformatics. I would rather write a wiki instead but you can deep dive into the references below 👇.

🏦 Database

ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs.

⁉️ Frequently Asked Questions (FAQ)

🙋 why are we calculating pubchem fingerprints even after calculating thelipinski descriptors ? what is the difference ?

👉 the lipinski descriptors will provide us with a set of a simple molecular descriptors that will provide us with the drug-like (oral drug) properties of the molecule (rule of five). in the other hand, thanks to the pubchem fingerprints, each molecule will be described by the unique building blocks, this will allow us to create a unique properties of the drug and that is the essence of drug discovery and drug design. the connectivity of the blocks are giving rise to the unique structure of the molecule and also the unique molecular properties. we have to find a way to rearrange the blocks in such a way that the molecule provides the most potency toward the target protein while also being safe and not so toxic.

📑 References & Research papers

• Alzheimer's disease - plaques, tangles, causes, symptoms & pathology
• What is Alzheimer's disease? - TED-Ed
• Efficacy of acetylcholinesterase inhibitors in Alzheimer's disease
• Treatment of dementia and Alzheimer's disease l Acetylcholinesterase inhibitors
• An Introduction to Computational Drug Discovery
• Computational Drug Discovery: Machine Learning for Making Sense of Big Data in Drug Discovery
• Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking
• Machine Learning for Drug Discovery (Explained in 2 minutes)

📝 License

MIT License

©️ Credits

Huge thanks to Mr. Chanin Nantasenamat for making this project possible through his clear explanation whether at the algorithmic level or at the domain-knowledge level 🙏