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.

通过图形表示或图像识别分子的化学结构是一项具有挑战性的模式识别任务，在药物开发方面具有很大的好处。我们提出了一种新的化学结构识别工具，该工具提供了最先进的性能，并可以适应少量数据样本和监督下的新领域。与以前的方法不同，我们的方法提供了原子级定位，因此可以将图像分割成不同的原子和键。我们的模型是第一个只使用 SMILES 监督进行原子级实体检测的 OCSR 模型。通过严格和广泛的基准测试，我们证明了我们的化学结构识别方法在数据效率、准确性和原子级实体预测方面的卓越性。