  Molecular structure recognition is the task of translating a molecular image
into its graph structure. Significant variation in drawing styles and
conventions exhibited in chemical literature poses a significant challenge for
automating this task. In this paper, we propose MolScribe, a novel
image-to-graph generation model that explicitly predicts atoms and bonds, along
with their geometric layouts, to construct the molecular structure. Our model
flexibly incorporates symbolic chemistry constraints to recognize chirality and
expand abbreviated structures. We further develop data augmentation strategies
to enhance the model robustness against domain shifts. In experiments on both
synthetic and realistic molecular images, MolScribe significantly outperforms
previous models, achieving 76-93% accuracy on public benchmarks. Chemists can
also easily verify MolScribe's prediction, informed by its confidence
estimation and atom-level alignment with the input image. MolScribe is publicly
available through Python and web interfaces:
https://github.com/thomas0809/MolScribe.
