Nature Communications (Jun 2021)
Algebraic graph-assisted bidirectional transformers for molecular property prediction
Abstract
Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Here, the authors propose an algebraic graph-assisted bidirectional transformer, which can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy and assisted with 3D stereochemical information from graphs.