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SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction

Authors :
Jiahui Zhang
Wenjie Du
Xiaoting Yang
Di Wu
Jiahe Li
Kun Wang
Yang Wang
Source :
Frontiers in Molecular Biosciences, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Molecular property prediction is a crucial task in various fields and has recently garnered significant attention. To achieve accurate and fast prediction of molecular properties, machine learning (ML) models have been widely employed due to their superior performance compared to traditional methods by trial and error. However, most of the existing ML models that do not incorporate 3D molecular information are still in need of improvement, as they are mostly poor at differentiating stereoisomers of certain types, particularly chiral ones. Also,routine featurization methods using only incomplete features are hard to obtain explicable molecular representations. In this paper, we propose the Stereo Molecular Graph BERT (SMG-BERT) by integrating the 3D space geometric parameters, 2D topological information, and 1D SMILES string into the self-attention-based BERT model. In addition, nuclear magnetic resonance (NMR) spectroscopy results and bond dissociation energy (BDE) are integrated as extra atomic and bond features to improve the model’s performance and interpretability analysis. The comprehensive integration of 1D, 2D, and 3D information could establish a unified and unambiguous molecular characterization system to distinguish conformations, such as chiral molecules. Intuitively integrated chemical information enables the model to possess interpretability that is consistent with chemical logic. Experimental results on 12 benchmark molecular datasets show that SMG-BERT consistently outperforms existing methods. At the same time, the experimental results demonstrate that SMG-BERT is generalizable and reliable.

Details

Language :
English
ISSN :
2296889X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Molecular Biosciences
Publication Type :
Academic Journal
Accession number :
edsdoj.f9c1167b1446492bbdff77c3125e043b
Document Type :
article
Full Text :
https://doi.org/10.3389/fmolb.2023.1216765