S-Space: Seoul National University
metadata only
Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer
Protein-protein interactions underlie biological complexity, and modeling their coevolution is essential for characterizing and engineering molecular assemblies. While protein and genomic language models have excelled at modeling individual proteins, extending these capabilities to protein complexes
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling#1141988
- Analysis of Quantized and Efficiently Adapted Protein Language Models#1155600
- Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.#354691
- Reinforcement Learning Guides Generative Protein Language Models#785044
- Reinforcement Learning Guides Generative Protein Language Models#783454
Record · ID 1121666
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive