ProtStructQA is a new executable benchmark for protein structural QA that identifies a capability threshold between 1.7B and 4B parameter models where effective prompting strategies shift from tool use to chain-of-thought.
ProteinGPT: Multimodal LLM for protein property prediction and structure understanding
6 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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VibeProteinBench is a new benchmark evaluating LLMs on open-ended language-interfaced protein design across recognition, engineering, and generation, with no model showing strong performance in all areas.
2D-ProteinRAG is a dual-dimensional RAG framework that incorporates BLAST workflows plus horizontal attribute alignment and vertical homology denoising to improve protein-text QA on both in-distribution and out-of-distribution cases.
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
STELLA aligns ESM3 bimodal sequence-structure encodings with Llama-3.1-8B text modeling to claim state-of-the-art results on protein functional description prediction and enzyme-catalyzed reaction prediction.
A cross-disciplinary review of 151 studies concludes LLMs accelerate research workflows while introducing recurring technical and ethical risks, including ten it flags as underexplored.
citing papers explorer
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ProtStructQA: A Denotation Threshold in Protein Structural Reasoning
ProtStructQA is a new executable benchmark for protein structural QA that identifies a capability threshold between 1.7B and 4B parameter models where effective prompting strategies shift from tool use to chain-of-thought.
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VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design
VibeProteinBench is a new benchmark evaluating LLMs on open-ended language-interfaced protein design across recognition, engineering, and generation, with no model showing strong performance in all areas.
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Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework
2D-ProteinRAG is a dual-dimensional RAG framework that incorporates BLAST workflows plus horizontal attribute alignment and vertical homology denoising to improve protein-text QA on both in-distribution and out-of-distribution cases.
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GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
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STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding
STELLA aligns ESM3 bimodal sequence-structure encodings with Llama-3.1-8B text modeling to claim state-of-the-art results on protein functional description prediction and enzyme-catalyzed reaction prediction.
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From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines
A cross-disciplinary review of 151 studies concludes LLMs accelerate research workflows while introducing recurring technical and ethical risks, including ten it flags as underexplored.