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Use ESMFold model to predict 3D structure of the input protein sequence.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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"]