English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Discovering new materials better suited to specific purposes is an importantissue in improving the quality of human life. Here, a neural network thatcreates molecules that meet some desired conditions based on a deepunderstanding of chemical language is proposed (Generative ChemicalTransformer, GCT). The attention mechanism in GCT allows a deeper understandingof molecular structures beyond the limitations of chemical language itselfwhich cause semantic discontinuity by paying attention to characters sparsely.It is investigated that the significance of language models for inversemolecular design problems by quantitatively evaluating the quality of thegenerated molecules. GCT generates highly realistic chemical strings thatsatisfy both chemical and linguistic grammar rules. Molecules parsed fromgenerated strings simultaneously satisfy the multiple target properties andvary for a single condition set. These advances will contribute to improvingthe quality of human life by accelerating the process of desired materialdiscovery.

Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention