Molecular Chirality

Molecular chirality, the non-superimposability of a molecule and its mirror image, significantly impacts its properties and interactions. Current research focuses on developing machine learning models, particularly graph neural networks (GNNs) and transformer architectures, to accurately predict and utilize chiral information in diverse applications, including drug discovery and materials science. These models are being enhanced with techniques like multiparameter persistent homology and novel message-passing schemes to better capture the three-dimensional aspects of chirality. Improved chiral recognition in these models promises to accelerate advancements in fields ranging from pharmaceutical development to materials design.