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.
ProtChatGPT: towards understanding proteins with large language models
3 Pith papers cite this work. Polarity classification is still indexing.
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Protein Thoughts uses hypothesis-guided entropy-regularized Tree-of-Thoughts search and embedding flow matching to achieve mean best-binder rank 11.2 and 91.08 Micro-F1 on SHS148k by keeping sequence, structure, interface, and chemical signals transparent.
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.
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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Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery
Protein Thoughts uses hypothesis-guided entropy-regularized Tree-of-Thoughts search and embedding flow matching to achieve mean best-binder rank 11.2 and 91.08 Micro-F1 on SHS148k by keeping sequence, structure, interface, and chemical signals transparent.
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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.