The rise of deep learning in various scientific and technology areas promotes the development of AI-based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R-groups), and visual contamination. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at Syntelly platform.
preprint version 1