Step-by-step drug discovery tutorials combining multiple CLI commands into complete workflows.

These recipes chain multiple CLI commands into complete, end-to-end drug discovery workflows — from structure prediction and docking to library screening. Reach for them when you want a worked, copy-and-adapt example rather than piecing commands together yourself, and when you're ready to turn a workflow into a reproducible script or CI/CD pipeline. Every step runs on the same backend and job history as the rest of the CLI, so intermediate results carry forward across commands. For the full command set, see the CLI overview.

Difficulty

intermediate

Workflow Recipes

Step-by-step drug discovery tutorials combining multiple CLI commands into complete workflows.

Recipe 1: Structure-Based Drug Discovery

Start from a protein sequence, predict its structure, prepare it for docking, find binding sites, dock a ligand, and evaluate drug-likeness.

Sequence → ESMFold → ProteinPrep → Sites → DiffDock → ADMET

ivybloom run esmfold protein_sequence=MKLVFFAEDVGSNKGAIIGLMVGGVVIA --follow

Output:

Job submitted: job_fold_001
Status: pending → running → completed (2m 15s)
pLDDT: 87.3 (Good confidence)

Download the predicted structure:

ivybloom jobs download job_fold_001 --output ./structures/

ivybloom proteinprep run ./structures/predicted_structure.pdb \
  --ph 7.4 \
  --detect-sites \
  --wait

Output:

Quality check: PASSED (no missing atoms, no clashes)
Hydrogens added: 1,234
Binding sites detected: 3

ivybloom proteinprep sites ./structures/predicted_structure.pdb \
  --method p2rank \
  --top-n 3

Output:

Site 1: Score 0.92, Center (12.3, 45.6, 78.9), Volume 823 Å³
Site 2: Score 0.78, Center (34.5, 67.8, 12.3), Volume 615 Å³
Site 3: Score 0.61, Center (56.7, 89.0, 34.5), Volume 498 Å³

ivybloom run diffdock \
  receptor=./structures/prepared.pdb \
  ligand="CC(=O)Oc1ccccc1C(=O)O" \
  --follow

ivybloom run admet smiles='CC(=O)Oc1ccccc1C(=O)O' --follow

Output:

Absorption: High intestinal absorption (94%)
Distribution: BBB penetrant
Metabolism: Low CYP inhibition
Excretion: t½ = 3.2h
Toxicity: Low risk (ProTox class IV)

Recipe 2: Ligand-Based Lead Optimization

Generate new molecular candidates based on a lead compound, evaluate their properties, and dock the best ones.

Lead SMILES → GenMol → ADMET Filter → Dock Top Hits

ivybloom orchestrate "Generate 50 molecules similar to aspirin (CC(=O)Oc1ccccc1C(=O)O) with improved solubility" --sync

Or via direct tool submission:

ivybloom run genmol \
  seed_smiles="CC(=O)Oc1ccccc1C(=O)O" \
  num_molecules=50 \
  --follow

Create a batch file admet-screen.yaml:

name: "Lead Optimization ADMET"
tool: admet
jobs:
  - name: "candidate-1"
    parameters: { smiles: "GENERATED_SMILES_1" }
  - name: "candidate-2"
    parameters: { smiles: "GENERATED_SMILES_2" }
options:
  parallel: 8
  stop_on_error: false

ivybloom batch submit admet-screen.yaml --wait
ivybloom batch collect BATCH_ID --format csv --output admet-results.csv

Filter the ADMET results for drug-like compounds, then dock them:

# Dock each hit against the target
ivybloom run diffdock \
  receptor=target.pdb \
  ligand="TOP_HIT_SMILES" \
  --follow

Recipe 3: Target Validation

Validate a protein target by checking sequence conservation, predicting structure, and analyzing binding site druggability.

Sequence → BLAST → ESMFold → ProteinPrep → Quality → Sites

ivybloom run blast \
  query_sequence=MKLVFFAEDVGSNKGAIIGLMVGGVVIA \
  database=nr \
  --follow

Review homologs and conservation scores to confirm the target is well-conserved across species.

ivybloom run esmfold \
  protein_sequence=MKLVFFAEDVGSNKGAIIGLMVGGVVIA \
  --follow

Check the pLDDT score — values above 70 indicate reliable structure prediction.

ivybloom proteinprep quality ./predicted_structure.pdb

Verify no missing atoms, clashes, or unusual residues.

ivybloom proteinprep sites ./predicted_structure.pdb \
  --method p2rank \
  --top-n 5 \
  --min-score 0.6

Sites with scores above 0.8 are considered highly druggable.

Recipe 4: Compound Library Screening

Import a compound library, screen it through ADMET, and export the filtered hits.

CSV Import → Batch ADMET → Filter → Export Hits

# Create a collection
ivybloom compounds create "Vendor Library" --description "2024 vendor catalog"

# Import compounds from CSV
ivybloom compounds import COLL_ID library.csv --skip-duplicates

Supported formats: CSV, SDF, SMILES. A CSV file needs a smiles column. The ivybloom mcp import tool also accepts smiles_column and name_column for non-standard headers.

Export the library to a format suitable for batch processing:

ivybloom compounds export COLL_ID --format csv --output library-export.csv

Create screen.yaml:

name: "Library ADMET Screen"
tool: admet
jobs:
  - name: "compound-001"
    parameters: { smiles: "CCO" }
  - name: "compound-002"
    parameters: { smiles: "CC(=O)Oc1ccccc1C(=O)O" }
options:
  parallel: 8
  stop_on_error: false

ivybloom batch submit screen.yaml --wait

Monitor progress:

ivybloom batch status BATCH_ID

# Collect all results
ivybloom batch collect BATCH_ID --format csv --output admet-results.csv

# Create a new collection with hits
ivybloom compounds create "ADMET Hits" --description "Drug-like compounds from vendor screen"

Running Recipes as Scripts

Any recipe can be converted into a shell script for automation:

#!/bin/bash
set -euo pipefail

export IVYBLOOM_API_KEY="${IVYBLOOM_API_KEY:?Set IVYBLOOM_API_KEY}"

# Fold → Prep → Dock pipeline
FOLD_RESULT=$(ivybloom run esmfold protein_sequence="$SEQUENCE" --agent --wait)
FOLD_JOB=$(echo "$FOLD_RESULT" | jq -r '.data.job_id')
ivybloom jobs download "$FOLD_JOB" --output ./structures/

ivybloom proteinprep run ./structures/predicted_structure.pdb \
  --ph 7.4 --detect-sites --wait

ivybloom run diffdock \
  receptor=./structures/prepared.pdb \
  ligand="$LIGAND_SMILES" \
  --agent --wait

See Agent Mode for structured JSON output in scripts.

Workflow Recipes

Difficulty

Workflow Recipes

Recipe 1: Structure-Based Drug Discovery

Recipe 2: Ligand-Based Lead Optimization

Recipe 3: Target Validation

Recipe 4: Compound Library Screening

Running Recipes as Scripts

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