Zero shot molecular generation via similarity kernels
Abstract Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key to their success is the close relationship bet...
Saved in:
| Main Authors: | Rokas Elijošius, Fabian Zills, Ilyes Batatia, Sam Walton Norwood, Dávid Péter Kovács, Christian Holm, Gábor Csányi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60963-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Positional embeddings and zero-shot learning using BERT for molecular-property prediction
by: Medard Edmund Mswahili, et al.
Published: (2025-02-01) -
A review on NLP zero-shot and few-shot learning: methods and applications
by: G. Ramesh, et al.
Published: (2025-08-01) -
A Survey of Zero-Shot Object Detection
by: Weipeng Cao, et al.
Published: (2025-05-01) -
Zero-Shot Sand-Dust Image Restoration
by: Fei Shi, et al.
Published: (2025-03-01) -
Novel kernel function for computing the similarity of text
by: Xiu-hong WANG, et al.
Published: (2012-12-01)