  Chemical formula is an artificial language that expresses molecules as text.
Neural machines that have learned chemical language can be used as a tool for
inverse molecular design. Here, we propose a neural machine that creates
molecules that meet some desired conditions based on a deep understanding of
chemical language (generative chemical Transformer, GCT). Attention-mechanism
in GCT allows a deeper understanding of molecular structures, beyond the
limitations of chemical language itself that cause semantic discontinuity, by
paying attention to characters sparsely. We investigate the significance of
language models to inverse molecular design problems by quantitatively
evaluating the quality of generated molecules. GCT generates highly realistic
chemical strings that satisfy both a chemical rule and grammars of a language.
Molecules parsed from generated strings simultaneously satisfy the multiple
target properties and are various for a single condition set. GCT generates de
novo molecules, and this is done in a short time that human experts cannot.
These advances will contribute to improving the quality of human life by
accelerating the process of desired material discovery.
