FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design

TL;DR

FuseDiff is a novel end-to-end diffusion model that jointly generates dual-target ligand poses with symmetry preservation, improving dual-target drug design accuracy and enabling systematic pose quality assessment.

Contribution

The paper introduces FuseDiff, a symmetry-preserving joint diffusion model for dual-target drug design, addressing limitations of prior staged approaches.

Findings

Achieves state-of-the-art docking performance on benchmarks.

Enables systematic dual-target pose quality assessment.

Supports dual-target pose generation with topological consistency.

Abstract

Dual-target structure-based drug design aims to generate a single ligand together with two pocket-specific binding poses, each compatible with a corresponding target pocket, enabling polypharmacological therapies with improved efficacy and reduced resistance. Existing approaches typically rely on staged pipelines, which either decouple the two poses via conditional-independence assumptions or enforce overly rigid correlations, and therefore fail to jointly generate two target-specific binding modes. To address this, we propose FuseDiff, an end-to-end diffusion model that jointly generates a ligand molecular graph and two pocket-specific binding poses conditioned on both pockets. FuseDiff features a message-passing backbone with Dual-target Local Context Fusion (DLCF), which fuses each ligand atom's local context from both pockets to enable expressive joint modeling while preserving the desired symmetries. Together with explicit bond generation, FuseDiff enforces topological consistency across the two poses under a shared graph while allowing target-specific geometric adaptation in each pocket. To support principled training and evaluation, we derive a dual-target training set and use an independent held-out test set for evaluation. Experiments on the benchmark and a real-world dual-target system show that FuseDiff achieves state-of-the-art docking performance and enables the first systematic assessment of dual-target pose quality prior to docking-based pose search.