Img2Mol - accurate SMILES recognition from molecular graphical depictions.

ABSTRACT: The automatic recognition of the molecular content of a molecule's graphical depiction is an extremely challenging problem that remains largely unsolved despite decades of research. Recent advances in neural machine translation enable the auto-encoding of molecular structures in a continuous vector space of fixed size (latent representation) with low reconstruction errors. In this paper, we present a fast and accurate model combining deep convolutional neural network learning from molecule depictions and a pre-trained decoder that translates the latent representation into the SMILES representation of the molecules. This combination allows us to precisely infer a molecular structure from an image. Our rigorous evaluation shows that Img2Mol is able to correctly translate up to 88% of the mol

SUBMITTER: Clevert DA 

PROVIDER: S-EPMC8565361 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature