https://github.com/laura-budurlean/drug-discovery-with-python-and-machine-learning

Explore data collection, analysis, and machine learning for drug discovery. Create predictive models, perform EDA, and deploy as web apps.
https://github.com/laura-budurlean/drug-discovery-with-python-and-machine-learning

biochemistry bioinformatics biostatistics biotechnology chemoinformatics computational-biology drug-discovery machine-learning pharmacokinetics pharmacology python qsar-modeling sklearn webapp

Last synced: 18 days ago
JSON representation

Explore data collection, analysis, and machine learning for drug discovery. Create predictive models, perform EDA, and deploy as web apps.

• Host: GitHub
• URL: https://github.com/laura-budurlean/drug-discovery-with-python-and-machine-learning
• Owner: laura-budurlean
• Created: 2024-12-27T19:01:13.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-01-16T05:10:24.000Z (21 days ago)
• Last Synced: 2025-01-16T06:22:58.538Z (21 days ago)
• Topics: biochemistry, bioinformatics, biostatistics, biotechnology, chemoinformatics, computational-biology, drug-discovery, machine-learning, pharmacokinetics, pharmacology, python, qsar-modeling, sklearn, webapp
• Homepage:
• Size: 1.02 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Drug Discovery with Python and Machine Learning

 ## Key Features: 

 - Scripts for collecting and preprocessing bioactivity data from the ChEMBL database. 

 

 - Tools for exploratory data analysis and calculating chemical descriptors. 

 

 - Python code for building and comparing machine learning models for predictive analytics. 

 

 - A framework for deploying QSAR models as web applications. 

 

 ## Learning Objectives:  

 

 - Apply Python and machine learning techniques to real-world bioinformatics challenges. 

 - Contribute to drug discovery efforts using computational drug discovery.

#### Based on the principles discussed in ML course designed by Chanin Nantasenamat.