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.
|State||Published - Dec 1 2021|
Bibliographical noteFunding Information:
Thework of Dong Chen, Xin Chen, Yi Jiang and Feng Pan was supported in part by the National Key R&D Program of China (2016YFB0700600). The work of Kaifu Gao and Guo-Wei Wei was supported in part by NSF grants DMS-2052983, DMS1761320, IIS1900473, NIH grants GM126189, and GM129004, Bristol-Myers Squibb, and Pfizer. The work of Duc Nguyen was supported in part by NSF grant DMS-2053284 and University of Kentucky start-up fund. The work of Dong Chen was also partly supported by Michigan State University.
© 2021, The Author(s).
ASJC Scopus subject areas
- Chemistry (all)
- Biochemistry, Genetics and Molecular Biology (all)
- Physics and Astronomy (all)