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+---
+title: "Common Workflow Patterns"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/991bd993/skills/datamol/references/workflow_patterns.md
+upstream_sha: 991bd993
+imported_at: 2026-08-08
+prompt_class: prompt
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Common Workflow Patterns
+
+Three end-to-end pipelines: data loading through filtering to analysis, structure-activity
+relationship analysis by scaffold series, and a virtual screening pipeline.
+
+## Common Workflows and Patterns
+
+### Complete Pipeline: Data Loading → Filtering → Analysis
+
+```python
+import datamol as dm
+import pandas as pd
+
+# 1. Load molecules
+df = dm.read_sdf("compounds.sdf")
+
+# 2. Standardize
+df['mol'] = df['mol'].apply(lambda m: dm.standardize_mol(m) if m else None)
+df = df[df['mol'].notna()]  # Remove failed molecules
+
+# 3. Compute descriptors
+desc_df = dm.descriptors.batch_compute_many_descriptors(
+    df['mol'].tolist(),
+    n_jobs=-1,
+    progress=True
+)
+
+# 4. Filter by drug-likeness
+druglike = (
+    (desc_df['mw'] <= 500) &
+    (desc_df['logp'] <= 5) &
+    (desc_df['hbd'] <= 5) &
+    (desc_df['hba'] <= 10)
+)
+filtered_df = df[druglike]
+
+# 5. Cluster and select diverse subset
+diverse_mols = dm.pick_diverse(
+    filtered_df['mol'].tolist(),
+    npick=100
+)
+
+# 6. Visualize results
+dm.viz.to_image(
+    diverse_mols,
+    legends=[dm.to_smiles(m) for m in diverse_mols],
+    outfile="diverse_compounds.png",
+    n_cols=10
+)
+```
+
+### Structure-Activity Relationship (SAR) Analysis
+
+```python
+# Group by scaffold
+scaffolds = [dm.to_scaffold_murcko(mol) for mol in mols]
+scaffold_smiles = [dm.to_smiles(s) for s in scaffolds]
+
+# Create DataFrame with activities
+sar_df = pd.DataFrame({
+    'mol': mols,
+    'scaffold': scaffold_smiles,
+    'activity': activities  # User-provided activity data
+})
+
+# Analyze each scaffold series
+for scaffold, group in sar_df.groupby('scaffold'):
+    if len(group) >= 3:  # Need multiple examples
+        print(f"\nScaffold: {scaffold}")
+        print(f"Count: {len(group)}")
+        print(f"Activity range: {group['activity'].min():.2f} - {group['activity'].max():.2f}")
+
+        # Visualize with activities as legends
+        dm.viz.to_image(
+            group['mol'].tolist(),
+            legends=[f"Activity: {act:.2f}" for act in group['activity']],
+            align=True  # Align by common substructure
+        )
+```
+
+### Virtual Screening Pipeline
+
+```python
+import numpy as np
+
+# 1. Calculate Tanimoto distances between query actives and library
+distances = dm.cdist(query_actives, library_mols, n_jobs=-1)
+
+# 3. Find closest matches (min distance to any query)
+min_distances = distances.min(axis=0)
+similarities = 1 - min_distances  # Convert distance to similarity
+
+# 4. Rank and select top hits
+top_indices = np.argsort(similarities)[::-1][:100]  # Top 100
+top_hits = [library_mols[i] for i in top_indices]
+top_scores = [similarities[i] for i in top_indices]
+
+# 5. Visualize hits
+dm.viz.to_image(
+    top_hits[:20],
+    legends=[f"Sim: {score:.3f}" for score in top_scores[:20]],
+    outfile="screening_hits.png"
+)
+```