# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for NVIDIA NIM (BioNeMo) ASR Biology API
  version: 1.0.0
extends: openapi/nvidia-nim-biology-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-10-01'
      generator: build-phrasing.py
      label: Generated by API Evangelist
      operations: 3
- target: $.paths['/v1/biology/nvidia/alphafold2/predict-structure-from-sequence'].post
  update:
    x-apievangelist-phrasing:
      intent: Predict a protein's 3D structure from its sequence
      effect: write
      questions:
      - Can I get a predicted 3D structure for a protein from just its amino acid sequence using AlphaFold2?
      - Which MSA databases, such as uniref90 or mgnify, can a protein folding prediction search?
      - Is there an option to relax the predicted protein structure after folding?
      instructions:
      - text: Predict the folded 3D structure of the amino acid sequence {sequence} with AlphaFold2.
        slots:
          sequence: requestBody.sequence
      - text: Fold protein sequence {sequence} using the {databases} MSA databases.
        slots:
          sequence: requestBody.sequence
          databases: requestBody.databases
      - text: Run AlphaFold2 on {sequence} with relaxation of the predicted structure set to {relax_prediction}.
        slots:
          sequence: requestBody.sequence
          relax_prediction: requestBody.relax_prediction
      method: generated
      generated: '2026-10-01'
- target: $.paths['/v1/biology/mit/diffdock'].post
  update:
    x-apievangelist-phrasing:
      intent: Dock a small-molecule ligand to a protein
      effect: write
      questions:
      - Can I predict where and how a drug-like molecule binds to my protein with DiffDock?
      - Does molecular docking accept the ligand as SMILES or SDF and the protein as a PDB file?
      - Can I ask for several candidate binding poses instead of a single docking result?
      instructions:
      - text: Dock ligand {ligand} against the PDB protein structure {protein} with DiffDock.
        slots:
          ligand: requestBody.ligand
          protein: requestBody.protein
      - text: Predict {num_poses} binding poses of {ligand} on protein {protein}.
        slots:
          num_poses: requestBody.num_poses
          ligand: requestBody.ligand
          protein: requestBody.protein
      - text: Run DiffDock for {ligand} on {protein} using {steps} diffusion steps.
        slots:
          ligand: requestBody.ligand
          protein: requestBody.protein
          steps: requestBody.steps
      method: generated
      generated: '2026-10-01'
- target: $.paths['/v1/biology/nvidia/molmim/generate'].post
  update:
    x-apievangelist-phrasing:
      intent: Generate small molecules around a seed SMILES
      effect: write
      questions:
      - Can I generate new drug-like molecules similar to a seed SMILES string with MolMIM?
      - Is there a way to optimize generated molecules toward a property while keeping a minimum similarity to the seed?
      - How many candidate molecules can a single MolMIM generation request return?
      instructions:
      - text: Generate new small molecules similar to the seed SMILES {smi}.
        slots:
          smi: requestBody.smi
      - text: Generate {num_molecules} molecules from seed {smi} using the {algorithm} algorithm.
        slots:
          num_molecules: requestBody.num_molecules
          smi: requestBody.smi
          algorithm: requestBody.algorithm
      - text: Optimize molecules from {smi} for {property_name}, keeping similarity to the seed above {min_similarity}.
        slots:
          smi: requestBody.smi
          property_name: requestBody.property_name
          min_similarity: requestBody.min_similarity
      method: generated
      generated: '2026-10-01'