ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning

• URL: http://arxiv.org/abs/2002.09914v1
• Date: Sun, 23 Feb 2020 14:30:55 GMT
• Title: ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
• Authors: Martijn Oldenhof, Adam Arany, Yves Moreau and Jaak Simm
• Abstract summary: In drug discovery, knowledge of the graph structure of chemical compounds is essential. A tool to analyze images automatically and convert them into a chemical graph structure would be useful for many applications. We develop a deep neural network model for optical compound recognition.
• Score: 6.88204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In drug discovery, knowledge of the graph structure of chemical compounds is essential. Many thousands of scientific articles in chemistry and pharmaceutical sciences have investigated chemical compounds, but in cases the details of the structure of these chemical compounds is published only as an images. A tool to analyze these images automatically and convert them into a chemical graph structure would be useful for many applications, such drug discovery. A few such tools are available and they are mostly derived from optical character recognition. However, our evaluation of the performance of those tools reveals that they make often mistakes in detecting the correct bond multiplicity and stereochemical information. In addition, errors sometimes even lead to missing atoms in the resulting graph. In our work, we address these issues by developing a compound recognition method based on machine learning. More specifically, we develop a deep neural network model for optical compound recognition. The deep learning solution presented here consists of a segmentation model, followed by three classification models that predict atom locations, bonds and charges. Furthermore, this model not only predicts the graph structure of the molecule but also produces all information necessary to relate each component of the resulting graph to the source image. This solution is scalable and could rapidly process thousands of images. Finally, we compare empirically the proposed method to a well-established tool and observe significant error reductions.

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