High-accuracy chemical structure restoration from molecular descriptors and its application to chemical design

Molecular descriptors are essential tools for analyzing compounds in drug discovery, but descriptors have a drawback - it is difficult to reconstruct the original compound using only descriptor data. To overcome this drawback, we used a deep learning Transformer model to restore the molecular structure from Morgan fingerprint (MF) data. We also explored compound optimization using numerical operations on the fingerprint vector.