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DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
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Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active compound. This approach preserves the essential molecular features of the original scaffold while introducing novel chemical elements or structural features to enhance potency, selectivity, or bioavailability. However, there is currently a lack of generative models specifically tailored for this task, especially in the pocket-conditioned context. In this work, we present DiffHopp, a conditional E(3)-equivariant graph diffusion model tailored for scaffold hopping given a known protein-ligand complex.
Forward citations
Cited by 2 Pith papers
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Atomic Design Transformer: Scaffold-Conditioned 3D Molecule Generation via xTB-Reward Reinforcement Learning
A plain causal transformer that tokenizes atom positions in local frames generates 3D molecules directly; RL against an xTB relaxation reward lifts topology-preserving valid yield from ~50% to ~95%.
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Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance
DiffSMol generates novel 3D binding molecules conditioned on ligand shape embeddings, and with shape or pocket guidance it reports substantial benchmark improvements over existing methods.
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