#protein-design

3 curated events

papersTODAY 04:00 UTC

AbFlow: Paratope-Centric Antibody Design via Interaction-Enhanced Flow Matching

A preprint introduces AbFlow, a generative approach that models full-atom antibody structures end-to-end while focusing on the paratope, the region that contacts the antigen. The method uses flow matching augmented with interaction information to guide design. The authors position it as addressing gaps in existing antibody design pipelines, which have lacked a unified generative framework at this structural resolution.

protein-design/flow-matching/generative-models/research

papersTODAY 04:00 UTC

ProteinZero Uses Online Reinforcement Learning for Self-Improving Protein Design

A new arXiv preprint introduces ProteinZero, a method that applies online reinforcement learning to protein generative models so they can improve without depending on curated sequence-structure datasets. The authors argue that current supervised training objectives are misaligned with actual protein design goals, and that their approach addresses this gap. The work appears as a replacement submission on arXiv's machine learning category.

protein-design/reinforcement-learning/generative-models/arxiv

papersTODAY 04:00 UTC

Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

A new arXiv paper introduces Chamaileon, a method for designing protein binders that works across different target contexts. The approach combines contextualized modeling with a mixed sampling strategy, building on recent generative techniques that jointly model protein sequence and structure. The authors position it as an alternative to existing end-to-end generation and hallucination-based pipelines, which they describe as limited in cross-context settings.

protein-design/generative-models/arxiv/structural-biology