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Daniel Probst

2 accepted papers

2025

Boosting Protein Graph Representations through Static-Dynamic Fusion

ICML 2025poster

Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynamics trajectories provides new opportunities…

Cited by 5SourcePDFScholar
2025

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

NeurIPS 2025poster

Generating diverse, all‐atom conformational ensembles of dynamic proteins such as G‐protein‐coupled receptors (GPCRs) is critical for understanding their function, yet most generative models simplify atomic detail or ignore conformational diversity altogether. We present latent diffusion for full pr…

Cited by 0SourcecodeScholar