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Controllable protein design with particle-based Feynman-Kac steering

Erik Hartman, Jonas Wallin, Johan Malmström, Jimmy Olsson · Nov 12, 2025 · Citations: 0

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Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology. Diffusion-based generative models have emerged as powerful tools for protein design, but steering them toward proteins with specified properties remains challenging. The Feynman-Kac (FK) framework provides a principled way to guide diffusion models using user-defined rewards. In this paper, we enable FK-based steering of RFdiffusion through the development of guiding potentials that leverage ProteinMPNN and structural relaxation to guide the diffusion process towards desired properties. We show that steering can be used to consistently improve predicted interface energetics and increase binder designability by $89.5\%$. Together, these results establish that diffusion-based protein design can be effectively steered toward arbitrary, non-differentiable objectives, providing a model-independent framework for controllable protein generation.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology."

Evaluation Modes

provisional (inferred)

None explicit

Validate eval design from full paper text.

"Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: No explicit eval keywords detected.
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology.
  • Diffusion-based generative models have emerged as powerful tools for protein design, but steering them toward proteins with specified properties remains challenging.
  • The Feynman-Kac (FK) framework provides a principled way to guide diffusion models using user-defined rewards.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

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