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

Searching new molecules in areas like drug discovery often starts from thecore structures of candidate molecules to optimize the properties of interest.The way as such has called for a strategy of designing molecules retaining aparticular scaffold as a substructure. On this account, our present workproposes a scaffold-based molecular generative model. The model generatesmolecular graphs by extending the graph of a scaffold through sequentialadditions of vertices and edges. In contrast to previous related models, ourmodel guarantees the generated molecules to retain the given scaffold withcertainty. Our evaluation of the model using unseen scaffolds showed thevalidity, uniqueness, and novelty of generated molecules as high as the caseusing seen scaffolds. This confirms that the model can generalize the learnedchemical rules of adding atoms and bonds rather than simply memorizing themapping from scaffolds to molecules during learning. Furthermore, despite therestraint of fixing core structures, our model could simultaneously controlmultiple molecular properties when generating new molecules.

Scaffold-based molecular design using graph generative model