TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

2026-07-24

AI Summary

This work addresses the challenge of designing molecular glues that induce ternary complexes—a problem involving modeling unknown protein–protein interfaces and jointly generating ligands with their corresponding complexes. Inspired by biological mechanisms, the authors propose a two-stage generative framework: first, an SE(3)-equivariant network estimates the protein–protein interface under geometric constraints; then, conditioned on this interface, the model jointly generates molecular glue compounds and predicts the rigid-body configuration of the resulting ternary complex. This approach represents the first end-to-end formulation of molecular glue design as a ternary complex generation task, integrating interface prediction with conditional ligand generation and thereby transcending conventional structure-based drug design paradigms. Experimental results demonstrate that the generated molecular glues are chemically plausible and yield structurally credible complexes, highlighting the method’s potential to accelerate molecular glue discovery.

Abstract

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://anonymous.4open.science/r/molecular-glue-design-806B.

Problem

• molecular glue
• ternary complex
• protein-protein interface
• targeted protein degradation
• computational design

Innovation

• molecular glue
• ternary complex generation
• SE(3)-equivariant modeling
• interface-conditioned generation
• generative flow matching