Getting Started

Welcome to the ChemXploreML documentation! ChemXploreML is a modular desktop application designed to simplify the process of predicting molecular properties using advanced machine learning techniques. This guide will help you quickly set up and start using ChemXploreML.

Citation

Marimuthu, A. N. & McGuire, B. A. Machine Learning Pipeline for Molecular Property Prediction Using ChemXploreML. J. Chem. Inf. Model. 65, 5424–5437 (2025). DOI: 10.1021/acs.jcim.5c00516

Download and Installation

Current Version: v4.6.2

Platform	Download Link
macOS (Apple Silicon)	Download
macOS (Intel - x64)	Download
macOS (Universal - Intel and Apple Silicon)	Download
Windows (x64)	Download
Linux (AppImage - x64)	Download

For other versions, please visit the GitHub releases page.

Initial Setup

1. Download the appropriate installer for your system.
2. Run the installer:
   • On macOS, open the .dmg file and drag the ChemXploreML app into your Applications folder.
   • On Windows, run the .exe installer and follow the on-screen instructions.
   • On Linux, you can download the .AppImage, .deb, or .rpm file from the releases page.

macOS Installation

macOS Gatekeeper Warning

macOS may prevent launching unsigned apps. To bypass this, after installing the app to your Applications folder, run the following command in your terminal:

xattr -c /Applications/ChemXploreML.app

This is a temporary workaround. A notarized version will be released in a future update.

Linux Installation

To run the AppImage on Linux, first make it executable:

chmod +x ChemXploreML-*.AppImage
./ChemXploreML-*.AppImage

Quick Start Guide

Follow these steps to train your first model with ChemXploreML:

1. Load Your Data

• Launch ChemXploreML.
• Navigate to the Load File tab and use the file browser to load your dataset (e.g., a .csv file).
• Configure the Column X (SMILES) and Column Y (target property) fields.

2. Generate Molecular Embeddings

• Go to the Embed Molecule tab.
• Select an embedding model (e.g., mol2vec).
• Click the Run button to generate the embeddings for your dataset. The embeddings will be saved as a .npy file.

3. Train Your Machine Learning Model

• Navigate to the ML Training tab.
• In the Model Panel, select a machine learning algorithm (e.g., Random Forest).
• In the Save Model Panel, specify a name for your trained model.
• Click Begin training.
• Once training is complete, you can view the results in the Results Panel.

4. Predict Molecular Properties

• Go to the ML Prediction tab.
• Load your trained model (.pkl file).
• Provide a file with new molecules to predict.
• Run the prediction to get the results.

What's Next?

Enjoy exploring your molecular datasets with ChemXploreML! For more detailed information, please refer to the other sections of this documentation.