Bioinformatics & Cheminformatics Project

This project demonstrates the use of Python and RDKit for drug discovery-related data analysis.
It involves collecting molecular datasets from ChEMBL, cleaning and curating them, applying filters like Lipinski’s Rule of Five, performing disruptor calculations, and generating visual plots.

---

📂 Project Contents

• Project work.ipynb
  Main workflow notebook. Includes:

  • Data collection from ChEMBL
  • Data cleaning and duplicate removal
  • Lipinski’s Rule of Five analysis
  • Disruptor calculation
  • Basic visualizations with RDKit and matplotlib

• aromatase.ipynb
  Focused case study on compounds related to aromatase inhibitors:

  • Data preparation
  • Rule of Five and disruptor screening
  • Plotting molecular property distributions

---

🛠️ Tools & Libraries Used

• Python 3.8+
• RDKit
• Pandas
• Matplotlib
• ChEMBL Database

---

🚀 How to Run

1. Clone this repository:

   git clone https://github.com/mike3119/https-github.com-mike3119-my-project.git
   
   

2. Navigate into the folder:

cd https-github.com-mike3119-my-project

3. Install dependencies:

pip install rdkit pandas matplotlib

4. Open the notebooks:

jupyter notebook

---

📊 Example Outputs

Some of the results you can expect:

Distribution plots of molecular weight, LogP, and other drug-likeness properties

Filtering of compounds based on Lipinski’s Rule of Five

Data tables of curated compounds for further analysis

---

🎯 Project Goals

Learn and apply bioinformatics/cheminformatics techniques for drug discovery.

Explore the power of ChEMBL as a molecular dataset resource.

Practice molecular analysis with RDKit in Python.

Build a foundation for advanced drug design projects.

---

👤 Author

Michael Hemen Akosu

hemenmonterakosu@gmail.com B.Sc. Chemistry

PGD in Drug Analysis, Pharmaceutical Chemistry (University of Ibadan)

Open to internships, collaborations, and learning opportunities in bioinformatics and cheminformatics.