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

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 topologica

SUBMITTER: Zhang J 

PROVIDER: S-EPMC10348360 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature