We lifted 7 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| concept-lab/shrec22_proteinligandbenchmark | canonical | 2 of 4 |
| lucagl/moad_ligandfinder | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| file_len | Ran | lucagl/moad_ligandfinder/lfetch.py code served (permissive licence) · get_code("ed9c49d7b4433159") |
| get_protein | Ran | concept-lab/shrec22_proteinligandbenchmark/evaluate.py pointer only (licence: NONE) · get_code("ab97938cd602e071") |
| secondsToStr | Ran | concept-lab/shrec22_proteinligandbenchmark/evaluate.py pointer only (licence: NONE) · get_code("2fcc867f7da412fc") |
| secondsToStr | Ran | lucagl/moad_ligandfinder/lfetch.py code served (permissive licence) · get_code("962f53c44819cfc1") |
| getStructureIterator | Not yet run | concept-lab/shrec22_proteinligandbenchmark/evaluate.py pointer only (licence: NONE) · get_code("7cb5e45c18271900") |
| getStructureIterator | Not yet run | concept-lab/shrec22_proteinligandbenchmark/filterLig.py pointer only (licence: NONE) · get_code("374bf8c4cbf86b3c") |
| queryMOAD | Not yet run | lucagl/moad_ligandfinder/lfetch.py code served (permissive licence) · get_code("7c0784a2fc12326d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2206.06035")
get_code_for_paper("2206.06035")
have("2206.06035")
Connect an agent — have() is free.