Paper

Co-folding model guided by structural proteomics

Unreadunread

AI summary

arXiv:2605. 26192v1 Announce Type: new Abstract: Protein structure generative models excel at predicting single protein static structures from sequence, but routinely fail to capture the correct conformational state of protein complexes, critical for protein design and induced proximity modalities such as antibodies and PROTACs.

Reading status

Priority

About this paper

Authors: Alon Shtrikman, Nitzan Simchi, Michal Ran Shchory, Sagie Brodsky, Eran Seger, Kirill Pevzner
Released: 2026-05-27 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Co-folding model guided by structural proteomics overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, correct, eval, rate, trained, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.26192

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Co-folding model guided by structural proteomics. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.