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https://github.com/AbhiLegend/DrugDisOpenVINO
https://github.com/AbhiLegend/DrugDisOpenVINO
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/AbhiLegend/DrugDisOpenVINO
- Owner: AbhiLegend
- Created: 2023-11-19T06:52:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-19T07:35:00.000Z (about 1 year ago)
- Last Synced: 2024-08-01T21:47:33.818Z (4 months ago)
- Language: Jupyter Notebook
- Size: 4.57 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-openvino - Drug Discovery “Lipophilicity” using OpenVINO toolkit - Finding Lipophilicity of peptides, proteins and molecules. (Table of content / Miscellaneous)
README
---
title: DrugDOpenV
emoji: 🐨
colorFrom: red
colorTo: yellow
sdk: streamlit
sdk_version: 1.28.2
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
To run the provided Python script using Streamlit, follow these steps:
### 1. Set Up Your Environment
Clone the repo
Create anaconda environment enable
run the command
pip install -r requirements.txt
```
Note: RDKit might require a specific installation process, especially on certain operating systems. Ensure you follow the official RDKit installation guidelines.
### 2. Prepare Your Script
`.
- Ensure that the model file (`lipophilicity_openvino.xml`) is accessible at the specified path in your script or update the `model_path` variable to the correct path where your model is stored.
### 3. Run the Streamlit App
- Open a terminal or command prompt.
- Navigate to the directory where your script is located.
- Run the script using Streamlit. For example, if your file is named `lipophilicity_app.py`, you would run:
```bash
streamlit run app.py
```
- Streamlit will start a local web server and provide you with a URL (usually `http://localhost:8501`).
### 4. Interact with the App
- Open the provided URL in a web browser.
- You'll see the Streamlit interface with the title "Lipophilicity Prediction App".
- Use the dropdown menu to select a SMILES string.
- Click the 'Predict Lipophilicity' button to see the prediction and the visual representation of the molecule.
### Additional Considerations
- Ensure that your environment supports all the necessary libraries and dependencies. For example, RDKit and OpenVINO have specific requirements for installation.
- If you plan to share this app or deploy it, consider using Streamlit sharing or another cloud service that supports Python and the required libraries.
- Be mindful of the computational resources required for running the model, especially if deploying this app for broader use.
Running a Streamlit app is generally straightforward and provides a powerful way to create interactive data appl