Investigation of chemical structure recognition by encoder-decoder models in learning progress.

ABSTRACT: 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

SUBMITTER: Nemoto S 

PROVIDER: S-EPMC10100163 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature