Img2Mol – accurate SMILES recognition from molecular graphical depictions. Issue 42 (12th October 2021)

• Record Type: Journal Article
• Authors: Clevert, Djork-Arné; Le, Tuan; Winter, Robin; Montanari, Floriane
• Is Part Of: Chemical science. Volume 12:Issue 42(2021)
• Page Start: 14174
• Page End: 14181
• Publication Date: 2021-10-12
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d1sc01839f
• Languages: English
• ISSNs: 2041-6520

Abstract

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. 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.