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LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
Jiaqi Guan · Xingang Peng · PeiQi Jiang · Yunan Luo · Jian Peng · Jianzhu Ma

Tue Dec 12 03:15 PM -- 05:15 PM (PST) @ Great Hall & Hall B1+B2 #105

Targeted protein degradation techniques, such as PROteolysis TArgeting Chimeras (PROTACs), have emerged as powerful tools for selectively removing disease-causing proteins. One challenging problem in this field is designing a linker to connect different molecular fragments to form a stable drug-candidate molecule. Existing models for linker design assume that the relative positions of the fragments are known, which may not be the case in real scenarios. In this work, we address a more general problem where the poses of the fragments are unknown in 3D space. We develop a 3D equivariant diffusion model that jointly learns the generative process of both fragment poses and the 3D structure of the linker. By viewing fragments as rigid bodies, we design a fragment pose prediction module inspired by the Newton-Euler equations in rigid body mechanics. Empirical studies on ZINC and PROTAC-DB datasets demonstrate that our model can generate chemically valid, synthetically-accessible, and low-energy molecules under both unconstrained and constrained generation settings.

Author Information

Jiaqi Guan (UIUC)
Xingang Peng (Peking University)
PeiQi Jiang (Tsinghua University, Tsinghua University)
Yunan Luo (University of Illinois at Urbana-Champaign)
Jian Peng (University of Illinois at Urbana-Champaign)
Jianzhu Ma (Tsinghua University)

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