Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task,Journal of Chemical Information and Modeling

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Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-07-06 , DOI: 10.1021/acs.jcim.2c00344
Ramil Nugmanov ₁ , Natalia Dyubankova ₁ , Andrey Gedich ₂ , Joerg Kurt Wegner ₃

Affiliation

This work introduces GraphormerMapper, a new algorithm for reaction atom-to-atom mapping (AAM) based on a transformer neural network adopted for the direct processing of molecular graphs as sets of atoms and bonds, as opposed to SMILES/SELFIES sequence-based approaches, in combination with the Bidirectional Encoder Representations from Transformers (BERT) network. The graph transformer serves to extract molecular features that are tied to atoms and bonds. The BERT network is used for chemical transformation learning. In a benchmarking study with IBM RxnMapper, which is the best AAM algorithm according to our previous study, we demonstrate that our AAM algorithm is superior to it on our “Golden” benchmarking data set.

中文翻译：

用于反应性理解的双向 Graphormer：针对反应原子到原子映射任务训练的神经网络

这项工作介绍了GraphormerMapper，这是一种基于变压器神经网络的反应原子到原子映射 (AAM) 的新算法，用于将分子图直接处理为原子和键的集合，而不是基于 SMILES/SELFIES 序列的方法，与来自 Transformers (BERT) 网络的双向编码器表示相结合。图形转换器用于提取与原子和键相关的分子特征。BERT 网络用于化学转化学习。在与 IBM RxnMapper 进行的基准测试研究中，这是我们之前研究中最好的 AAM 算法，我们证明了我们的 AAM 算法在我们的“黄金”基准测试数据集上优于它。

更新日期：2022-07-06

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