⌘+K

Pulse Explore Clubs Researchers Journals

Home Clubs Explore

January 1, 2025Digital DiscoveryOpen Access

Going beyond SMILES enumeration for data augmentation in generative drug discovery

Helena Brinkmann Antoine Argante Hugo ter Steege Francesca Grisoni

View Full Paper

Ask AI

Bookmark

Share

Key Points

New SMILES augmentation methods enhance the capabilities of generative drug design.
These strategies expand the tools available for chemical language modelling in drug discovery.
The work indicates potential advancements in data augmentation for cheminformatics.
Improved methodologies may lead to enhanced generative models in pharmaceutical research.

Implication

Novel strategies broaden data augmentation in chemical language modelling, suggesting improvements in generative drug design.

Abstract

We propose novel SMILES augmentation strategies for chemical language modelling, which broaden the range of tools for generative drug design.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Brinkmann et al. (2025) studied this question.

synapsesocial.com/papers/68c194029b7b07f3a0618694 https://doi.org/10.1039/d5dd00028a

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models2024 · 3 citations
2PromptSMILES: Prompting for scaffold decoration and fragment linking in chemical language models2024 · 1 citations
3Improving the Reliability of Molecular String Representations for Generative Chemistry2025 · 9 citations
4Phenotypic Profile-Informed Generation of Drug-Like Molecules via Dual-Channel Variational Autoencoders2025 · 2 citations
5Phenotypic Profile-Informed Generation of Drug-Like Molecules via Dual-Channel Variational Autoencoders2025 · 1 citations