Getting Started with Chemetrian
Updated 10/12/2025
Welcome to Chemetrian! We are enabling chemists to leverage machine learning in their research and development workflows.
This guide will serve to:
1. Educate on machine learning in chemistry.2. Describe how to use Chemetrian's tools.
Table of Contents
1. Foundations of machine learning in chemistry
- Molecular Featurization - Learn how to convert molecular structures into numerical representations suitable for ML algorithms
- Bayesian Optimization - Learn how Bayesian Reaction Optimization can optimize reaction parameter spaces with a minimal number of experiments
2. Chemetrian Tools
- Chemical Space Analysis - Visualize, explore, and filter chemical diversity using dimensionality reduction and clustering techniques
- Predictive Modeling - Build robust models for molecular property prediction using state-of-the-art algorithms
- Descriptor Calculation - Compute quantum chemical and classical cheminformatics descriptors for molecules
- Bayesian Reaction Optimization - Optimize large parameter spaces with a minimum number of experiments by leveraging active learning
3. Getting Started
- Platform Overview - Introduction to Chemetrian's capabilities and philosophy
- Getting Help - Support resources and guidance
About Chemetrian
Chemetrian is a comprehensive machine learning platform designed specifically for chemical research. Our platform combines cutting-edge ML techniques with domain expertise to accelerate discovery and optimization in chemistry. Whether you're predicting molecular properties, analyzing chemical space, or optimizing reaction conditions, Chemetrian provides the tools and education you need to integrate ML into your research workflow.
Platform Philosophy
We believe that education is fundamental to successful ML adoption in chemistry. Our platform not only provides powerful tools but also comprehensive learning resources that help chemists understand both the theoretical foundations and practical applications of machine learning in their field. This documentation serves as both a user guide and an educational resource for incorporating ML into chemical research.
1. Foundations of machine learning in chemistry
Molecular Featurization
Learn how to convert molecular structures into numerical representations suitable for ML algorithms. This fundamental step is crucial for any machine learning application in chemistry, as it transforms complex molecular information into a format that algorithms can process effectively.
Bayesian Optimization
Learn how Bayesian Reaction Optimization can optimize reaction parameter spaces with a minimal number of experiments relative to design of experiments or traditional approaches.
2. Chemetrian Tools
Chemical Space Analysis
Visualize, explore, and filter chemical diversity using dimensionality reduction and clustering techniques. This tool helps you understand the relationships between different molecules and identify patterns in your chemical data.
Predictive Modeling
Build machine learning models to predict molecular properties and chemical outcomes. Upload training data, select features, train models using various algorithms, and make predictions on new molecules before testing them in the lab. Use feature importance analysis to understand the fundamentals driving your chemistry.
Descriptor Calculation
Compute quantum chemical and classical cheminformatics descriptors for molecules. These descriptors include electronic and geometric information about molecules, making them information-rich and interpretable to the chemist.
Bayesian Reaction Optimization
Optimize large parameter spaces with a minimum number of experiments by leveraging active learning.
Getting Help
For additional support, visit our Support Center or contact our team.
