SMILES2DTA(DTC)_Demo

🔬 SMILES2DTA(DTC)_Demo is a Streamlit web application that predicts drug-target binding affinity using a trained deep learning model. It processes drug SMILES strings and protein sequences as inputs and provides the predicted binding affinity.

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Related Publication

This project is based on the following published research paper:

SMILES2DTA: a CNN-based approach for identifying drug candidates and predicting drug-target binding affinity
[Hasanul Mukit, Sayeed Hossain, Mirza Milan Farabi, Mehrab Zaman Chowdhury, Ahmed Iqbal Pritom & Humayan Kabir Rana]
[Neural Computing & Applications by Springer], [2024].
Link: [https://doi.org/10.1007/s00521-024-10814-x]

Please check the publication for a detailed explanation of the model and methodology.

Features

• Accepts drug SMILES strings and protein sequences as inputs.
• Predicts binding affinity using a trained CNN model.
• User-friendly interface with clean and modern design.

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How It Works

• The user enters the drug's SMILES string and the protein sequence.
• The app tokenizes the inputs, pads them to a fixed length, and passes them to the trained model.
• The predicted binding affinity is displayed in the app.

Technologies Used

• Jupyter Notebook
• Python
• TensorFlow/Keras
• Streamlit
• Pickle

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Acknowledgments

• This dashboard was created as part of a research project to simplify and improve drug-target binding affinity prediction.