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Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design

ICML 2025 · Image generation › Fast diffusion sampling · approach: Training-free diffusion acceleration · 0 citations (OpenAlex)

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Proposes RERD, an inference-time iterative refinement framework for reward-guided diffusion sampling that alternates partial noising and reward-guided denoising, evaluated on protein structure and cell-type-specific DNA enhancer design.

Problem

Single-shot reward-guided diffusion sampling cannot correct errors introduced by approximate value functions, and struggles with hard constraints (e.g., cell-type specificity) in protein and DNA design.

Approach

Iterate noising (forward pre-trained policy to a low noise level K) and reward-guided denoising, with the denoising step combining per-sample importance sampling over the reverse steps and SMC-style resampling across the batch at the final step, akin to an evolutionary algorithm; initialize from feasible designs to handle hard constraints. Theory shows the output targets exp(r/alpha) p_pre under i

Key results

• On protein cRMSD, RERD reaches P50 1.68 vs 8.2 for SVDD (lower is better).
• On HepG2 enhancer design, RERD reaches P50 7.9 and P95 9.1 vs 2.3 and 2.8 for SVDD.
• On ss-match, RERD achieves P50 0.86 vs 0.66 for SVDD, with similar log-likelihood.

Measured effects reported in the paper

Surprising findings

• Recovery rates on cRMSD inverse folding are lower than ProteinMPNN, which is a specialized conditional model, though the authors describe performance as competitive.

Limitations

• Theoretical guarantee relies on assumptions (exact soft-optimal policy approximation, matching marginals) that hold only approximately in practice.
• Evaluated only on protein and DNA design; small molecule design left as future work.
• Compared against limited baselines (SMC, SVDD, GA) without classifier-guidance or gradient-based methods.

Experimental setup

Datasets: UniRef (EvoDiff pretraining) MPRA enhancer activity dataset (~700k 200-bp sequences) protein reference structures

Benchmarks: protein design rewards (ss-match, cRMSD, globularity, symmetry) DNA enhancer design (HepG2, K562, SKNSH)

Metrics: P50 reward P95 reward per-residue log-likelihood pLDDT pTM diversity recovery rate

Baselines: SMC SVDD GA

Builds on: SVDD SMC guidance masked discrete diffusion EvoDiff

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