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

• URL: http://arxiv.org/abs/2607.22143v1
• Date: Fri, 24 Jul 2026 09:41:29 GMT
• Title: TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
• Abstract summary: We formulate molecular glue design as a ternary complex generation problem and propose a biology-inspired generative framework, TriGlue.<n>Motivated by the mechanism of molecular glue action, we decompose ternary complex generation into two coupled stages: interface estimation and interface-conditioned complex generation.
• Score: 9.836807699408041
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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