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.
Applying inter- pretable machine learning in computational biology—pitfalls, recommendations and opportunities for new developments.Nature Methods, 21(8):1454–1461
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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.