用氨基酸片段引导的推测解码，让蛋白质生成更快更可信。
SpecMER: Fast Protein Generation with K-mer Guided Speculative Decoding
- 用多序列比对提取的氨基酸片段作为生物先验，指导生成
- 速度提升24%-32%，生成序列更符合真实蛋白质特征
- 适合需要快速生成高可信度蛋白序列的研究场景
自回归模型已推动蛋白质工程发展，可生成自然界不存在的新蛋白序列。但其串行推理导致延迟高，限制了高通量筛选的应用。推测解码通过轻量级草稿模型预采样，由大模型验证并优化，可加速生成。然而，在蛋白质生成中，草稿模型通常忽略目标蛋白的结构与功能约束，导致生成序列生物学上不合理，且分布偏移。我们提出SpecMER（基于k-mer引导的推测解码），利用多序列比对提取的k-mer motif引入生物学、结构和功能先验。通过并行评分候选序列，并选择最符合已知生物模式的序列，SpecMER显著提升生成序列的合理性，同时保持推测解码的高效性。相比标准自回归解码，SpecMER实现24%-32%的速度提升，接受率更高，序列似然值也更好。
原文摘要 · Abstract (English)
Autoregressive models have transformed protein engineering by enabling the generation of novel protein sequences beyond those found in nature. However, their sequential inference introduces significant latency, limiting their utility in high-throughput protein screening. Speculative decoding accelerates generation by employing a lightweight draft model to sample tokens, which a larger target model then verifies and refines. Yet, in protein sequence generation, draft models are typically agnostic to the structural and functional constraints of the target protein, leading to biologically implausible outputs and a shift in the likelihood distribution of generated sequences. We introduce SpecMER (Speculative Decoding via k-mer Guidance), a novel framework that incorporates biological, structural, and functional priors using k-mer motifs extracted from multiple sequence alignments. By scoring candidate sequences in parallel and selecting those most consistent with known biological patterns, SpecMER significantly improves sequence plausibility while retaining the efficiency of speculative decoding. SpecMER achieves 24-32% speedup over standard autoregressive decoding, along with higher acceptance rates and improved sequence likelihoods.
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