论文标题
编码模型在学习进度中对化学结构识别的研究
Investigation of chemical structure recognition by encoder-decoder models in learning progress
论文作者
论文摘要
使用Encoder $ - $ - $ - $解码器（ED）模型的描述符生成方法作为输入很有用，因为描述符的连续性和对结构的可重新性性的连续性。但是，尚不清楚在ED模型的学习进度中如何识别结构。在这项工作中，我们创建了各种学习进度的ED模型，并研究了结构信息与学习进步之间的关系。我们表明，使用基于子结构的描述符监视下游任务的准确性和输入$ - $ - $输出子结构相似性，这表明基于下游任务的准确性可能不够敏感，以评估ED模型的性能不足以评估smiles a smiles a smiles a smiles a smiles a smiles a smiles a smiles a smiles node smiles，这表明复合子结构是在ED模型早期学习的。另一方面，我们证明了结构修复是时间$ - $ - $消耗，尤其是学习不足导致比实际结构更大的结构的估计。可以推断，确定结构的终点是模型的艰巨任务。据我们所知，这是第一个将ED模型微笑的学习进度与多种化学物质的化学结构联系起来的研究。
Descriptor generation methods using latent representations of encoder$-$decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized in the learning progress of ED models. In this work, we created ED models of various learning progress and investigated the relationship between structural information and learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input$-$output substructure similarity using substructure$-$based descriptors, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time$-$consuming, and in particular, insufficient learning led to the estimation of a larger structure than the actual one. It can be inferred that determining the endpoint of the structure is a difficult task for the model. To our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals.
