We lifted 9 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| thunlp-mt/pepglad | canonical | 7 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| aar | Ran | thunlp-mt/pepglad/evaluation/seq_metric.py code served (permissive licence) · get_code("99b400302703c232") |
| clamp_coord | Ran | thunlp-mt/pepglad/generate.py code served (permissive licence) · get_code("f2916ae470191082") |
| compute_rmsd | Ran | thunlp-mt/pepglad/evaluation/rmsd.py code served (permissive licence) · get_code("a1113bf1f4b79892") |
| get_best_ckpt | Ran | thunlp-mt/pepglad/generate.py code served (permissive licence) · get_code("f7ef153bd4e8be1a") |
| slide_aar | Ran | thunlp-mt/pepglad/evaluation/seq_metric.py code served (permissive licence) · get_code("e1b9a31622a34ae1") |
| struct_diversity | Ran | thunlp-mt/pepglad/evaluation/diversity.py code served (permissive licence) · get_code("9ad6907b79ff7bd0") |
| to_device | Ran | thunlp-mt/pepglad/generate.py code served (permissive licence) · get_code("8fb15b5e77827ed9") |
| create_encoder | Not yet run | thunlp-mt/pepglad/models/autoencoder/model.py code served (permissive licence) · get_code("5bebb16ab75fb874") |
| url_get | Not yet run | thunlp-mt/pepglad/utils/network.py code served (permissive licence) · get_code("2d15d2649443d0f6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom \textbf{Pep}tide design with \textbf{G}eometric \textbf{LA}tent \textbf{D}iffusion (PepGLAD) given the binding site. We first establish a benchmark consisting of both 1D sequences and 3D structures from Protein Data Bank (PDB) and literature for systematic evaluation. We then identify two major challenges of leveraging current diffusion-based models for peptide design: the full-atom geometry and the variable binding geometry. To tackle the first challenge, PepGLAD derives a variational autoencoder that first encodes full-atom residues of variable size into fixed-dimensional latent representations, and then decodes back to the residue space after conducting the diffusion process in the latent space. For the second issue, PepGLAD explores a receptor-specific affine transformation to convert the 3D coordinates into a shared standard space, enabling better generalization ability across different binding shapes. Experimental Results show that our method not only improves diversity and binding affinity significantly in the task of sequence-structure co-design, but also excels at recovering reference structures for binding conformation generation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.13555")
get_code_for_paper("2402.13555")
have("2402.13555")
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