We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jostorge/diffusion-hopping | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| str_to_bool | Ran | jostorge/diffusion-hopping/train_model.py code served (permissive licence) · get_code("60288e0371fe1b60") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.07416")
get_code_for_paper("2308.07416")
have("2308.07416")
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