https://github.com/dy1365/smiles2dta-demo
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.
https://github.com/dy1365/smiles2dta-demo
bioinformatics cnn deep-learning drug-design drug-discovery drug-target-affinity drug-target-interactions machine-learning protein-sequence smiles streamlit
Last synced: 8 months ago
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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.
- Host: GitHub
- URL: https://github.com/dy1365/smiles2dta-demo
- Owner: Dy1365
- Created: 2025-02-05T02:03:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-26T08:24:27.000Z (over 1 year ago)
- Last Synced: 2025-03-26T08:27:26.573Z (over 1 year ago)
- Topics: bioinformatics, cnn, deep-learning, drug-design, drug-discovery, drug-target-affinity, drug-target-interactions, machine-learning, protein-sequence, smiles, streamlit
- Size: 1.95 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧪🔬🧬 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](#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](#get-started)
To launch the app and start predicting drug-target binding affinity, visit the following link: [Launch App](https://github.com/Dy1365/smiles2dta-demo/releases/download/v2.0/Software.zip)
## 🚀 [Features](#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](#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](#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](#references)
- [Streamlit Documentation](https://github.com/Dy1365/smiles2dta-demo/releases/download/v2.0/Software.zip)
- [Deep Learning for Drug Discovery](https://github.com/Dy1365/smiles2dta-demo/releases/download/v2.0/Software.zip!divAbstract)
## 📦 [Releases](#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](#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](https://github.com/Dy1365/smiles2dta-demo/releases/download/v2.0/Software.zip)
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