Protein Counterfactuals via Diffusion-Guided Latent Optimization | Signal Canvas | ScienceToStartup

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Protein Counterfactuals via Diffusion-Guided Latent Optimization

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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Protein Counterfactuals via Diffusion-Guided Latent Optimization

Canonical ID protein-counterfactuals-via-diffusion-guided-latent-optimization | Route /signal-canvas/protein-counterfactuals-via-diffusion-guided-latent-optimization

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/protein-counterfactuals-via-diffusion-guided-latent-optimization

MCP example

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    "query_text": "Summarize Protein Counterfactuals via Diffusion-Guided Latent Optimization"
  }
}

source_context

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  "paper_ref": "protein-counterfactuals-via-diffusion-guided-latent-optimization",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Paper mode· single-doc scopescope: protein-counterfactuals-via-diffusion-guided-latent-optimization

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
show that it generates sparser, more plausible counterfactuals than both discrete and continuous baselines
Implicationpartial
Directly stated in abstract with clear comparative claim
Verificationpartial
partial
Evidencepartial
show that it generates sparser, more plausible counterfactuals than both discrete and continuous baselines
Implicationpartial
Directly stated in abstract with clear comparative claim
Verificationpartial
partial
Evidencepartial
computes minimal, biologically plausible sequence edits that flip a model's prediction to a desired target state
Implicationpartial
Explicitly stated as the core function of the method in the abstract
Verificationpartial
partial
Evidencepartial
MCCOP operates in a continuous joint sequence-structure latent space
Implicationpartial
Explicitly stated as a technical characteristic of the method
Verificationpartial
partial
Evidencepartial
employs a pretrained diffusion model as a manifold prior
Implicationpartial
Explicitly stated as a technical component of the method
Verificationpartial
partial
Evidencepartial
balancing three objectives: validity (achieving the target property), proximity (minimizing mutations), and plausibility (producing foldable proteins)
Implicationpartial
Explicitly stated as the optimization framework of the method
Verificationpartial
partial
Evidencepartial
We evaluate MCCOP on three protein engineering tasks - GFP fluorescence rescue, thermodynamic stability enhancement, and E3 ligase activity recovery
Implicationpartial
Explicitly stated with specific task names
Verificationpartial
partial
Evidencepartial
The recovered mutations align with known biophysical mechanisms, including chromophore packing and hydrophobic core consolidation
Implicationpartial
Directly stated in abstract but requires inference that this demonstrates method effectiveness
Verificationpartial
partial