DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model (Updated March 17, 2024).
In: Drug Week, 2024-04-02, S. 727-727
serialPeriodical
Zugriff:
DrugHIVE is a deep hierarchical structure-based generative model that utilizes artificial intelligence (AI) to enhance molecular generation in drug design. This model surpasses current methods in terms of performance and speed, and it can be applied to various drug design tasks such as de novo generation, molecular optimization, scaffold hopping, linker design, and high throughput pattern replacement. The scalability of DrugHIVE allows it to be used with high confidence AlphaFold predicted receptors, expanding the ability to generate high-quality drug-like molecules. However, it is important to note that this preprint has not yet undergone peer review. [Extracted from the article]
Copyright of Drug Week is the property of NewsRx and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model (Updated March 17, 2024).
|
---|---|
Zeitschrift: | Drug Week, 2024-04-02, S. 727-727 |
Veröffentlichung: | 2024 |
Medientyp: | serialPeriodical |
ISSN: | 1531-6440 (print) |
Sonstiges: |
|