Difficulty in chirality recognition for Transformer architectures learning chemical structures from string

Understand

Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure.

• However, little research has been done on how these models understand chemical structure.
• To address this black box, we investigated the relationship between the learning progress of SMILES and chemical structure using a representative NLP model, the Transformer.
• We show that while the Transformer learns partial structures of molecules quickly, it requires extended training to understand overall structures.