面向生物序列设计的柔性流生成方法
Flexible Flows for Biological Sequence Design
AI 导读
一项被 NeurIPS 2026 接收的研究提出面向生物序列设计的改进离散流匹配（DFM）方法。该方法通过编码序列元素间领域偏好的结构化耦合、基于共享全局潜变量的变长编辑操作参数化、潜空间 classifier-free guidance 及 Dirichlet 先验温度缩放，在密度估计、DNA 序列生成和肽序列生成等任务上达到 SOTA，代码已开源。
正文 · 原文
Abstract:Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flow Matching (DFM) offers a generative framework over such spaces, but existing approaches rely on biologically uninformative couplings and offer limited flexibility for variable-length sequence generation and fine-grained control. We propose a structured coupling that encodes domain-specific preferences among sequence elements, biasing the source distribution toward plausible regions without modifying the flow objective or training procedure. Building on this, we introduce a latent edit-based rate parameterization that models variable-length generation via edit operations conditioned on a shared global latent, akin to a latent variable model, while remaining tractable. We further introduce a latent classifier-free guidance mechanism that steers generation coherently in continuous latent space, along with Dirichlet-prior temperature scaling for test-time control over edit operations. Our method achieves state-of-the-art performance across diverse biological sequence tasks, including density estimation, unconditional and conditional DNA sequence generation, and peptide sequence generation. Our code is available at this https URL.
| Comments: | Accepted at NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2606.10543 [cs.LG] |
| (or arXiv:2606.10543v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10543 arXiv-issued DOI via DataCite |
Submission history
From: Yogesh Verma [view email]
[v1]
Tue, 9 Jun 2026 08:11:14 UTC (3,884 KB)
[v2]
Wed, 11 Nov 2026 10:58:03 UTC (3,884 KB)
来源：arXiv cs.AI · arxiv.org