القائمة
Use ESMFold model to predict 3D structure of the input protein sequence.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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-esmfold |
| description | Use ESMFold model to predict 3D structure of the input protein sequence. |
| 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 pred_protein_structure_esmfold.
Use the ESMFold model for protein 3D structure prediction.
Args:
sequence (str): Protein sequence
Return:
status: success/error
msg: message
pdb_path (str): The predicted pdb file path
How to use tool pred_protein_structure_esmfold :
response = await client.session.call_tool(
"pred_protein_structure_esmfold",
arguments={
"sequence": sequence
}
)
result = client.parse_result(response)
pred_protein_structure = result["pdb_path"]