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MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction. | LitMetric

AI Article Synopsis

  • Accurate prediction of molecular properties is crucial for drug design, but traditional methods need extensive expertise and often struggle with limited data and generalization issues.
  • The study introduces molecular graph BERT (MG-BERT), which uses graph neural networks integrated with the BERT model and employs a self-supervised learning approach called masked atoms prediction for better performance with unlabeled data.
  • Experimental results show that MG-BERT consistently outperforms state-of-the-art methods in predicting various molecular properties, offering improved interpretability and eliminating the need for manually crafted features.

Article Abstract

Motivation: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability.

Results: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.

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Source
http://dx.doi.org/10.1093/bib/bbab152DOI Listing

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