nobel321 / DNN-metabolite-identifier

A deep neural network that trains and tests using the Massbank database to identify metabolites.

Compound Property Prediction using Deep Neural Network

This Python program predicts compound properties such as molecular formula and exact mass using a Deep Neural Network (DNN) algorithm. The program retrieves data from a MySQL database, preprocesses it, trains a DNN model, and then uses the trained model to make predictions on new data.

Article Explanation: https://medium.com/@opemipo404/identifying-metabolites-with-deep-neural-networks-556e8c61ebb8

Getting Started

Prerequisites

• Python 3.x
• pandas
• scikit-learn
• tensorflow (or any other deep learning framework of your choice)
• mysql-connector-python

Installation

1. Clone the repository to your local machine:

git clone https://github.com/yourusername/compound-property-prediction.git

2. Install the required libraries using pip:

pip install pandas scikit-learn tensorflow mysql-connector-python

Usage

1. Set up a MySQL database with the required data. Update the database connection details (e.g., user, password, host, database) in the cnx object in the main.py file.
2. Run the main.py file to run the program.

Customization

You can customize the model architecture, hyperparameters, and other settings by modifying the code in the main.py file. You can also update the preprocessing steps in the main.py file according to your specific requirements.

Contributing

If you would like to contribute to this project, please follow the standard GitHub fork and pull request workflow.

License

This project is licensed under the MIT License.

Contact

If you have any questions, suggestions, or issues, please feel fre to contact me at [e-mail].