Martijn Oldenhof, Edward De Brouwer, Adam Arany, Yves Moreau
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Identifying the chemical structure from a graphical representation or image of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet existing methods for chemical structure recognition do not typically generalize well and show diminished effectiveness when confronted with domains where data is sparse or costly to generate such as hand-drawn molecule images. To address this limitation we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches our method provides atom-level localization and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency accuracy and atom-level entity prediction.