MMCP-GEN：面向条件蛋白质序列生成的模态可扩展扩散语言模型
MMCP-GEN: A Modality-Extensible Diffusion Language Model for Conditional Protein Sequence Generation
其「可学习查询 + 模态指示头」的可组合条件机制，可迁移到视觉扩散模型的多条件控制（如同时以深度、分割、文本为条件），实现新增条件时无需重训主干；生成-打分联合目标也可借鉴用于序列与结构一致性的对齐。
原文依据 a modality-composable and extensible conditioning mechanism that fuses heterogeneous biological conditions via learnable queries and modality-indicator heads
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期扩散语言模型（DLM）的进展在从头蛋白质设计上展现出显著潜力。然而，实现可控的蛋白质生成需要整合多种生物条件，例如结构、功能与化学相互作用，而每种条件都以不同的模态表示。现有方法往往只支持单一条件，或通过各自独立的模态专用编码器来处理多种条件。这种隔离限制了跨模态交互，降低了生成质量，并使引入新条件时必须重新训练或重新设计主干网络。为解决这些局限，我们提出 MMCP-GEN，一个用于多模态、多条件蛋白质序列生成（Multi-Modal, Multi-Condition Protein sequence GENeration）的扩散语言模型。MMCP-GEN 为复杂多模态约束下的可控蛋白质生成建立了新范式。其核心是一种模态可组合且可扩展的条件机制，通过可学习查询与模态指示头融合异构生物条件，从而在无需重新训练主干网络的情况下实现解耦、可扩展且跨模态的条件整合。一个联合的生成与打分目标进一步使序列恢复与结构保真度相对齐。在实验上，MMCP-GEN 在结构条件、功能条件与配体条件任务上均取得最优性能，序列恢复率最高提升 5%，并在多种功能标注任务上优于基于注意力的基线方法。这些结果确立了 MMCP-GEN 作为可控蛋白质生成的通用且高保真框架的地位。源代码已公开于 https://github.com/WanyuGroup。
英文原文摘要 1655 字符
Recent advances in diffusion-based language models (DLMs) have shown remarkable potential for de novo protein design. However, enabling controllable protein generation requires integrating diverse biological conditions, such as structure, functions, and chemical interactions, each represented in distinct modalities. Existing approaches often either support a single condition or treat multiple conditions through separate modality-specific encoders. This isolation limits cross-modal interaction, reduces generation quality, and complicates the incorporation of new conditions without retraining or redesigning the backbone. To address these limitations, we introduce MMCP-GEN, a DLM for Multi-Modal, Multi-Condition Protein sequence GENeration. MMCP-GEN establishes a new paradigm for controllable protein generation under complex multimodal constraints. Its core is a modality-composable and extensible conditioning mechanism that fuses heterogeneous biological conditions via learnable queries and modality-indicator heads, enabling disentangled, extensible, and cross-modal condition integration without retraining the backbone. A joint generation-and-scoring objective further aligns sequence recovery with structural fidelity. Empirically, MMCP-GEN achieves state-of-the-art performance across structure-, function-, and ligand-conditioned tasks, improving sequence recovery by up to 5% and outperforming attentive baselines in diverse functional annotation tasks. These results establish MMCP-GEN as a general and high-fidelity framework for controllable protein generation. The source code is publicly available at https://github.com/WanyuGroup.