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

ABSTRACT: 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 on molecular structures, resulting in less intuitive representations. Moreover, the widely used message passing mechanism is limited to providing 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). The GeoT learns molecular graph structures through attention-based mechanisms specifically designed to offer reliable interpretability as well as molecular property prediction. Consequently, the GeoT can generate attention maps

SUBMITTER: Kwak B 

PROVIDER: S-EPMC10601421 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature