The multilayer Perceptron algorithm trained on Chemoinformatics dataset of LRRK2 Gene inhibitors to screen the new potential inhibitors.
LRRK2-Blocker is a deep learning-based virtual screening system for identifying potential inhibitors of the LRRK2 protein. LRRK2 is a promising drug target for Parkinson's disease, and machine learning-based virtual screening systems are increasingly replacing traditional computer-aided drug design screening systems.
LRRK2-Blocker takes in high-dimensional molecular descriptors of chemical compounds as input and predicts their inhibition ability against the LRRK2 protein. This allows researchers to quickly and efficiently identify potential drug candidates without having to rely on time-consuming and expensive laboratory experiments.
The development of LRRK2-Blocker is a significant step forward in the search for new and effective treatments for Parkinson's disease. By enabling the rapid identification of potential drug candidates, LRRK2-Blocker could help to accelerate the development of new therapies and improve the lives of millions of patients.
- Linux OS
- Python 3
- Conda
- Git
You can clone the repository using the following command:
git clone https://github.com/aysanraza/lrrk2-blocker.git
- 0.1
- Initial Release
This project is licensed under the MIT license - see the LICENSE.md file for details
- Ahsan Raza