Molecular-InChI: Automated Recognition of Optical Chemical Structure

With the advent of a new era dominated by digital media and publications in recent years, the importance of striking a balance between traditional and new modes of operation has become increasingly apparent. It has been standard practice in the field of chemistry for decades to express chemical compounds using their structural forms, referred to as the Skeletal formula. In this research, we tried to interpret these old chemical structure images, extracted from old literature, to transform pictures back to the underlying chemical structure labeled as InChI text using a huge set of synthetic image data produced by Bristol-Myers Squibb. In this paper, we propose an improved synthetic data and an Encoder-Decoder-based deep learning-based model to automatically represent these molecular images into their underlying InChI representation.