English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent years, molecular representation learning has emerged as a key areaof focus in various chemical tasks. However, many existing models fail to fullyconsider the geometric information of molecular structures, resulting in lessintuitive representations. Moreover, the widely used message-passing mechanismis limited to provide the interpretation of experimental results from achemical perspective. To address these challenges, we introduce a novelTransformer-based framework for molecular representation learning, named theGeometry-aware Transformer (GeoT). GeoT learns molecular graph structuresthrough attention-based mechanisms specifically designed to offer reliableinterpretability, as well as molecular property prediction. Consequently, GeoTcan generate attention maps of interatomic relationships associated withtraining objectives. In addition, GeoT demonstrates comparable performance toMPNN-based models while achieving reduced computational complexity. Ourcomprehensive experiments, including an empirical simulation, reveal that GeoTeffectively learns the chemical insights into molecular structures, bridgingthe gap between artificial intelligence and molecular sciences.

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