Investigation of chemical structure recognition by encoder-decoder models in learning progress. (arXiv:2210.16307v1 [physics.chem-ph])

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.