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

Frontiers in Molecular Biosciences, ISSN: 2296-889X, Vol: 10, Page: 1216765
2023
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Research on Molecular Bioscience Published by Researchers at University of Science and Technology of China (SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction)

2023 JUL 17 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Life Science Daily -- Current study results on molecular bioscience have been

Article Description

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.

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