Algebraic graph-assisted bidirectional transformers for molecular property prediction

Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei () and Feng Pan ()
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Dong Chen: Peking University, Shenzhen Graduate School
Kaifu Gao: Michigan State University
Duc Duy Nguyen: University of Kentucky
Xin Chen: Peking University, Shenzhen Graduate School
Yi Jiang: Peking University, Shenzhen Graduate School
Guo-Wei Wei: Michigan State University
Feng Pan: Peking University, Shenzhen Graduate School

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as a variety of machine learning algorithms, including decision trees, multitask learning, and deep neural networks. We validate the proposed AGBT framework on eight molecular datasets, involving quantitative toxicity, physical chemistry, and physiology datasets. Extensive numerical experiments have shown that AGBT is a state-of-the-art framework for molecular property prediction.

Date: 2021
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DOI: 10.1038/s41467-021-23720-w

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