GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

Understand

In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks.

• However, many existing models fail to fully consider the geometric information of molecular structures, resulting in less intuitive representations.
• Moreover, the widely used message-passing mechanism is limited to provide the interpretation of experimental results from a chemical perspective.
• To address these challenges, we introduce a novel Transformer-based framework for molecular representation learning, named the Geometry-aware Transformer (GeoT).