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arXiv Machine Learning By Jianliang Wu, Anjie Qiao, Zhen Wang, Zhewei Wei, Sheng Chen

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

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FuseDiff is an end‑to‑end diffusion model designed for dual‑target structure‑based drug design, jointly generating a ligand graph and two pocket‑specific binding poses conditioned on both target pockets. It employs a message‑passing backbone with Dual‑target Local Context Fusion (DLCF) to fuse ligand atom contexts from both pockets, preserving symmetry while enabling expressive joint modeling. The model enforces topological consistency across the two poses and allows target‑specific geometric adaptation, achieving state‑of‑the‑art docking performance and enabling systematic assessment of dual‑target pose quality before docking‑based pose search.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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