零样本生成环肽，通过可组合几何约束实现
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
- 将环化模式拆解为可组合的几何约束，输入扩散模型
- 在不同环化策略下成功生成目标结合肽，成功率38%至84%
- 无需环肽训练数据，适合新靶点药物设计
环肽因其在结构上存在的几何约束而具备优于线性肽的生化特性，为解决未满足的医学需求提供了新可能。然而，由于训练数据有限，靶向环肽的设计仍处于探索阶段。为此，我们提出CP-Composer，一种基于可组合几何约束的零样本环肽生成框架。该方法将复杂环化模式分解为基本约束单元，并通过节点与边的几何条件嵌入扩散模型。训练时，模型学习线性肽中约束单元及其随机组合；推理时，将新型环化所需约束组合作为输入。实验表明，尽管仅使用线性肽训练，该模型仍能生成多样化的靶向结合环肽，在不同环化策略下的成功率介于38%至84%之间。
原文摘要 · Abstract (English)
Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical needs. However, designing target-specific cyclic peptides remains underexplored due to limited training data. To bridge the gap, we propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation via composable geometric constraints. Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model through geometric conditioning on nodes and edges. During training, the model learns from unit constraints and their random combinations in linear peptides, while at inference, novel constraint combinations required for cyclization are imposed as input. Experiments show that our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84% on different cyclization strategies.
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