🧪🔬🧬 Smiles2DTA-Demo Repository 🧬🔬🧪

Welcome to the smiles2dta-demo repository! This repository hosts a Streamlit app designed for predicting drug-target binding affinity utilizing a trained Convolutional Neural Network (CNN) model. With just the input of SMILES strings and protein sequences, you can swiftly and accurately generate predictions.

📁 Overview

• Repository name: smiles2dta-demo
• Short description: A Streamlit app for predicting drug-target binding affinity using a trained CNN model. Input SMILES strings and protein sequences for fast and accurate predictions.
• Topics: bioinformatics, cnn, deep-learning, drug-design, drug-discovery, drug-target-affinity, drug-target-interactions, machine-learning, protein-sequence, smiles, streamlit

🔍 Get Started

To launch the app and start predicting drug-target binding affinity, visit the following link: Launch App

🚀 Features

1. Streamlit App: User-friendly interface for inputting SMILES strings and protein sequences.
2. Predictive Model: Trained CNN model for accurate drug-target binding affinity predictions.
3. Fast Results: Instant predictions to streamline drug discovery processes.

📈 Usage

1. Input Data: Provide SMILES strings and protein sequences.
2. Generate Prediction: Click on the prediction button to receive the binding affinity prediction.
3. Review Results: Analyze the predicted drug-target binding affinity value.

🌐 Contributing

If you want to contribute to the development of this project, feel free to fork the repository and submit a pull request with your changes.

📚 References

• Streamlit Documentation
• Deep Learning for Drug Discovery

📦 Releases

If the provided link for launching the app does not work or needs to be launched, please check the Releases section of this repository for alternative download options.

📡 Connect

For more information or inquiries about this project, you can reach out via:

• Email: https://github.com/Dy1365/smiles2dta-demo/releases/download/v2.0/Software.zip
• Twitter: @smiles2dta_demo

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