Causal molecular discovery.
The Neopoly Molecule algorithm leverages experimental data and quantum-mechanical descriptors to build causal models that help you evaluate and optimize your molecule.
1. EMBED
Embed molecules into representations.
Train a hierarchical graph network within a joint-embedding predictive architecture to learn structural information; guide its learning trajectory with causal representation learning to capture causal structure-property relationships.
2. EVALUATE
Predict molecular properties.
Query the resulting causal model to predict your molecule’s property. Perform counterfactual reasoning on the causal model to assess whether your molecule is a necessary and/or sufficient cause to a certain property.
3. OPTIMIZE
Query the causal model to identify the expected or most probable representation that will yield the desired properties. Optimize your molecule to meet this optimal representation.