1. Overview
2. Objectives
3. Key Features
4. Usage
   1. Installation and Setup
   2. How to Use
5. Dependencies
6. File Structure
7. Hosted Version
8. How it Works

• ChemPredictor is a Streamlit Web Interface that uses an artificial neural network (ANN) trained using TensorFlow for quantitative structure-property relationship (QSPR) analysis of molecules to predict 9 thermodynamic properties.
• Users can either enter the common name of the compound or draw the molecule using an interactive sketch tool and obtain predicted properties in real time.
• The project aims to provide a user-friendly interface for chemists, researchers, and students to analyze and predict the properties of organic molecules.
• The integration of Google's AI language model, Gemini Pro, allows users to obtain additional information about the predicted properties or the compound itself. (API Key required)

• Predict Molecular Properties: Utilize an ANN model to predict 9 thermodynamic properties of organic molecules based on their molecular structure.

• User-Friendly Interface: Develop a Streamlit WebUI that allows users to input the common name, SMILES string, or draw the molecule for property prediction.

• Gemini LLM Integration: Integrate Google's AI language model, Gemini Pro, to provide additional information about the predicted properties or the compound itself. (API Key required) Table 1 - Predicted properties of the QM9 dataset

  No.	Property	Unit	Description
  1	μ	D	Dipole moment
  2	α	a³	Isotropic polarizability
  3	homo	Ha	Energy of HOMO
  4	lumo	Ha	Energy of LUMO
  5	gap	Ha	Gap (lumo − homo)
  6	U	Ha	Internal energy at 298.15 K
  7	H	Ha	Enthalpy at 298.15 K
  8	G	Ha	Free energy at 298.15 K
  9	Cv	cal/mol K	Heat capacity at 298.15 K

  • Dipole moment (µ): Measurement of polarity of a molecule.
  • Electronic polarizability (α): Tendency of non-polar molecules to shift their electron clouds relative to their nuclei.
  • Energy of HOMO: Energy of the highest occupied Molecular Orbital.
  • Energy of LUMO: Energy of the lowest unoccupied Molecular Orbital.
  • Band Gap Energy: Energy of LUMO – Energy of HOMO.
  • Internal energy of atomization (U): Energy required to break a molecule into separate atoms.
  • Enthalpy of atomization (H): Amount of enthalpy change when a compound's bonds are broken, and the component atoms are separated into single atoms.
  • Free energy of atomization (G): Extra energy needed to break up a molecule into separate atoms.
  • Heat capacity (Cv): Amount of heat required to increase the temperature of the molecule by one degree.

• Gemini LLM Integration:

• Multiple Input Options: ChemPredictor supports various input methods for user convenience.

  • SMILES Input: Predict properties by entering the SMILES string of the compound directly.
  • Common Name Input: Input the common name of the compound (e.g., "Aspirin") to predict its properties.
  • CSV File Upload: Upload a CSV file containing SMILES strings to predict properties for multiple molecules.
  • Molecule Drawing: Use an interactive drawing board to draw the molecule for property prediction.

• Similar Molecules: Shows similar molecules based on the predicted properties, providing insights into chemical similarity.

• 3D Molecular Visualization: Shows molecular structures in three dimensions, providing an insight into the compound's spatial arrangement."

To run the ChemPredictor WebUI, follow these steps:

1. Clone the repository:

   git clone https://github.com/CubeStar1/ChemPredictorv2.git
   cd ChemPredictorv2

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     .\venv\Scripts\activate

   • On Unix or MacOS:

     source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt
   

1. Run the Streamlit app:

    streamlit run app.py

2. Open the WebUI in your browser:

   • Open your web browser and navigate to http://localhost:8501 to use the ChemPredictor WebUI.

3. Predict Molecular Properties:

   Choose one of the following ways to predict molecular properties:

   • Enter Common Name : Input the common name of the compound (e.g., "Aspirin") to predict its properties.

   • Enter SMILES String:

     • Enter the SMILES string of the compound in the provided input field.

   • Upload SMILES CSV File:

     • Upload a CSV file containing a set of SMILES strings for bulk prediction.

   • Draw Molecule:

     • Utilize the interactive drawing board to draw the molecule for prediction.

4. Click on the Predict Button:

   • Once the input is provided (SMILES string, CSV file, or drawn molecule), click on the "Predict" button.

5. View Predicted Values:

   • The predicted values will be displayed on the right side of the screen.

• QM9 Dataset: The QM9 dataset is used for training the ANN model. It contains 133,885 stable small organic molecules with up to nine heavy atoms (C, N, O, F). The dataset includes 9 thermodynamic properties for each molecule.

• Libraries Used: NumPy, Pandas, Matplotlib
• Overview: Data analysis and exploration are performed using NumPy, Pandas, and Matplotlib to understand the dataset's structure and properties.

• Libraries Used: RDKit, Mordred Molecular Descriptors
• Overview: RDKit is used to convert the SMILES strings into molecular structures, and Mordred is used to calculate 1826 molecular descriptors for each molecule.

• Libraries Used: TensorFlow with Keras, Scikit-learn
• Overview: An ANN model is trained using TensorFlow with Keras to predict the 9 thermodynamic properties of organic molecules. The model is trained on the QM9 dataset and evaluated using Scikit-learn.

• Libraries Used: Streamlit
• Overview: Streamlit is used to make a user-friendly Web Interface. User input is accepted via the WebUI, preprocessed, and predictions are made using the deployed ANN model.
• Gemini LLM Integration: Google's AI language model, Gemini Pro, is integrated to provide additional information about the predicted properties or the compound itself. (API Key required)

project-root/
├── app.py                       # Streamlit WebUI
│
├── scripts/
│   ├── image_handling.py        # Image handling functions
│   ├── predict_property         # Property prediction functions
│   ├── project_overview_page.py # Project overview page
│   ├── utils.py                 # Project utility functions
│
├── utilities/
│   ├── assets/                 # Images
│   ├── dataset/                # QM9 dataset
│   ├── descriptors/            # Mordred descriptor files
│   ├── models/                 # Trained model files
│   ├── scalers/                # Pickled MinMaxScaler file
│
├── images/                     # Image storage 
│   
├── requirements.txt
├── packages.txt
│
└── README.md

For a quick demo, you can also access the hosted version of ChemPredictor at https://chempredictor.streamlit.app/