Papers
1–4 of 4Protein Counterfactuals via Diffusion-Guided Latent Optimization
Deep learning models can predict protein properties with unprecedented accuracy but rarely offer mechanistic insight or actionable guidance for engineering improved variants. When a model flags an ant...
EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering
We introduce EvoFlows, a variable-length sequence-to-sequence protein modeling approach uniquely suited to protein engineering. Unlike autoregressive and masked language models, EvoFlows perform a lim...
ProtAlign: Contrastive learning paradigm for Sequence and structure alignment
Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into consideration. Traditi...
How to make the most of your masked language model for protein engineering
A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill th...