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molclaw-pack-sidechains
Predicts full-atom sidechain conformations from backbone PDBs using AttnPacker for structure preparation workflows.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Predicts full-atom sidechain conformations from backbone PDBs using AttnPacker for structure preparation workflows.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.
Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.
Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.
Chroma toolkit skill covering chroma_monomer for single-chain generation, chroma_complex for multi-chain assembly generation, and chroma_symmetry for symmetry-constrained protein design.
Retrieve SMILES strings from PubChem database using compound names.
Generate new molecules de novo.
| name | molclaw-pack-sidechains |
| description | Predicts full-atom sidechain conformations from backbone PDBs using AttnPacker for structure preparation workflows. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Note:
molclaw-file-transfer before execution.molclaw-pdbfixer before execution.molclaw-scp-server to complete tool invocation.The description of tool pack_sidechains.
Predict full-atom sidechain conformations from backbone PDBs for protein structure preparation workflows.
Args:
input_pdb (str): Input PDB file path, required.
device (str|None): Compute device such as cuda:0, default None (auto by source script).
chunk_size (int): Inference chunk size for long proteins, default 500.
no_post_process (bool): Skip rotamer post-processing for faster runtime, default False.
max_optim_iters (int): Maximum optimization iterations in post-process, default 250.
steric_wt (float): Steric clash penalty weight, default 1.0.
optim_repeats (int): Post-process optimization repeats, default 2.
dry_run (bool): Create a traceable run directory without running inference, default False.
Return:
status (str): 'success', 'error', or 'partial_success'.
msg (str): Human-readable execution message.
input_pdb (str): Input PDB path used for this run.
output_dir (str): Unique run directory under tool_result/pack_sidechains_result.
output_pdb (str): Expected or generated output PDB path.
device (str|None): Device value used for execution.
chunk_size (int): Chunk size used.
no_post_process (bool): Whether post-process was skipped.
max_optim_iters (int): Max optimization iterations used.
steric_wt (float): Steric weight used.
optim_repeats (int): Optimization repeats used.
dry_run (bool): Whether dry-run mode was used.
error_type (str, optional): Exception type when status is 'error'.
traceback (str, optional): Python traceback when status is 'error'.
How to use tool pack_sidechains :
response = await client.session.call_tool(
"pack_sidechains",
arguments={
"input_pdb": "/path/to/input.pdb",
"device": "cuda:0",
"chunk_size": 500,
"no_post_process": False,
"max_optim_iters": 250,
"steric_wt": 1.0,
"optim_repeats": 2,
"dry_run": False
}
)
result = client.parse_result(response)
output_pdb = result["output_pdb"]
# 1) Main mode
{
"input_pdb": "/path/to/input.pdb",
"device": "cuda:0",
"chunk_size": 500,
"no_post_process": False,
"max_optim_iters": 250,
"steric_wt": 1.0,
"optim_repeats": 2,
"dry_run": False
}
# 2) Variant mode
{
"input_pdb": "relative/path/to/test_backbone.pdb",
"chunk_size": 500,
"dry_run": True
}