{"id":25675625,"url":"https://github.com/haripasapuleti/web_apps","last_synced_at":"2026-05-16T15:03:10.972Z","repository":{"id":263119273,"uuid":"849309610","full_name":"HariPasapuleti/Web_Apps","owner":"HariPasapuleti","description":"This web app predicts the solubility (LogS) of molecules based on their SMILES representation. 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The app utilizes machine learning and molecular descriptors to make predictions and visualize the results.\r\n\r\n## Features\r\n\r\n- **Molecular Descriptor Calculation**: The app computes key molecular descriptors such as MolLogP, MolWt, and AromaticProportion.\r\n- **SMILES Input**: Users can input molecular structures as SMILES strings.\r\n- **Solubility Prediction**: The app uses a pre-trained model to predict the solubility (LogS) of the molecules.\r\n- **Interactive Interface**: Built with Streamlit for easy use and interaction.\r\n\r\n## Technologies Used\r\n\r\n- **Streamlit**: For building the web application.\r\n- **RDKit**: For calculating molecular descriptors from SMILES strings.\r\n- **scikit-learn**: For loading the pre-trained machine learning model to make predictions.\r\n- **Pandas \u0026 NumPy**: For data manipulation.\r\n- **Pickle**: For saving and loading the trained model.\r\n\r\n## Getting Started\r\n\r\nTo get started with this project locally, follow the steps below:\r\n\r\n### Prerequisites\r\n\r\nMake sure you have the following installed:\r\n\r\n- Python 3.7+\r\n- pip (Python package manager)\r\n\r\n### Installation\r\n\r\n1. Clone the repository to your local machine:\r\n\r\n    ```bash\r\n    git clone https://github.com/HariPasapuleti/Web_Apps.git\r\n    cd Web_Apps\r\n    ```\r\n\r\n2. Install the necessary dependencies:\r\n\r\n    ```bash\r\n    pip install -r requirements.txt\r\n    ```\r\n\r\n3. Run the app:\r\n\r\n    ```bash\r\n    streamlit run app.py\r\n    ```\r\n\r\n4. Open the app in your browser.\r\n\r\n### Input\r\n\r\n- Users can input molecular structures in the form of SMILES strings through the sidebar. The default SMILES input is:\r\nNCCCC CCC CN\r\n\r\n\r\n### Output\r\n\r\nThe app will display the following outputs:\r\n\r\n- **Molecular Descriptors**: LogP, Molecular Weight, Number of Rotatable Bonds, and Aromatic Proportion.\r\n- **Predicted LogS Value**: The predicted solubility value for the input molecule.\r\n\r\n## Model Details\r\n\r\nThe app uses a pre-trained machine learning model saved in the file `solubility_model.pkl`. The model takes in molecular descriptors and predicts the solubility of the molecule (LogS).\r\n\r\n## Deployed Link\r\n\r\nYou can access the deployed web app here:\r\n[Deployed Molecular Solubility Prediction Web App](https://molecular-solubility.streamlit.app/)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharipasapuleti%2Fweb_apps","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharipasapuleti%2Fweb_apps","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharipasapuleti%2Fweb_apps/lists"}