Molecular Property Prediction

The Imperative

A molecule might bind perfectly to its target but fail as a drug because it's toxic, metabolized too quickly, or can't reach the right tissue. Predicting these properties computationally eliminates countless failed experiments.

Our Approach

We develop machine learning models that predict critical molecular properties from structure alone—before synthesis, before testing, before investment.

Key Methodologies

• •Graph neural networks: Treating molecules as graphs of atoms and bonds, learning structure-property relationships
• •Quantum machine learning: Incorporating quantum mechanical calculations for accurate property prediction
• •Attention mechanisms: Identifying which molecular substructures drive specific properties
• •Ensemble models: Combining multiple algorithms for robust, reliable predictions