# TriGlue：受生物学启发的分子胶诱导三元复合物生成模型

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-04 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmsgv6bqi004wroxzo7camlkp
- 原文链接：https://arxiv.org/abs/2607.22143

## AI 摘要

TriGlue将分子胶设计建模为三元复合物生成问题，提出受生物学启发的生成框架，分两阶段完成：SE(3)-等变界面估计模块预测几何约束的蛋白-蛋白界面，界面条件三元流匹配网络联合生成分子胶并预测组装三元复合物的刚体变换。实验表明TriGlue能生成化学有效分子和合理三元复合物，代码已开源。

## 正文

Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown protein-protein interface and requires the simultaneous modeling of ligand generation, protein-protein docking, and ternary complex assembly. In this work, we formulate molecular glue design as a ternary complex generation problem and propose a biology-inspired generative framework, TriGlue. Motivated by the mechanism of molecular glue action, we decompose ternary complex generation into two coupled stages: interface estimation and interface-conditioned complex generation. First, we develop an SE(3)-equivariant interface estimation module that predicts a geometrically constrained protein-protein interface from unbound monomer structures. Second, we introduce an interface-conditioned ternary flow matching network that jointly generates the molecular glue and predicts the rigid-body transformation required to assemble the ternary complex. Extensive experiments demonstrate that TriGlue generates chemically valid molecules and produces plausible ternary complexes, which highlight the potential of biology-inspired generative modeling for accelerating molecular glue discovery. Our code is available at https://github.com/yuliangyan0807/molecular-glue-design.