arXiv (OAI Expanded)
open access
Reinforcement Learning Guides Generative Protein Language Models
Protein engineering can optimize molecules for biotechnology and therapeutics, but navigating the high-dimensional sequence landscape remains challenging. Protein language models (pLMs) have shown to to generate functional proteins far from natural sequences, yet their outputs tend to reflect preval
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
- Reinforcement Learning Guides Generative Protein Language Models#785044
- Large language models generate functional protein sequences across diverse families#413810
- 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
- Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology#485165
Record · ID 783454
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