Img2Mol – accurate SMILES recognition from molecular graphical depictions† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d1sc01839f
Ontology highlight
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 molecular depictions into their SMILES representation. A pretrained version of Img2Mol is made publicly available on GitHub for non-commercial users. 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.
SUBMITTER: Clevert D
PROVIDER: S-EPMC8565361 | biostudies-literature |
REPOSITORIES: biostudies-literature
ACCESS DATA