PPI2Text generates natural-language captions for protein-protein interactions from sequences by encoding each protein with ESM3, building a residue-pair map, and decoding with Qwen3 using coordinate-aligned positional encoding.
arXiv preprint arXiv:2405.06649 , year=
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Proteo-R1 decouples an MLLM-based understanding expert that selects functional residues from a diffusion-based generation expert that builds protein structures under those explicit constraints.
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.
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.
citing papers explorer
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PPI2Text: Captioning Protein-Protein Interactions with Coordinate-Aligned Pair-Map Decoding
PPI2Text generates natural-language captions for protein-protein interactions from sequences by encoding each protein with ESM3, building a residue-pair map, and decoding with Qwen3 using coordinate-aligned positional encoding.
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Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
Proteo-R1 decouples an MLLM-based understanding expert that selects functional residues from a diffusion-based generation expert that builds protein structures under those explicit constraints.
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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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The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.