bio-admet-prediction

## Version Compatibility

Reference examples tested with: RDKit 2024.03+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# ADMET Prediction

**"Predict the drug-likeness and toxicity of my compounds"** → Estimate ADMET properties (bioavailability, CYP inhibition, hERG liability, toxicity) for candidate molecules using the ADMETlab 3.0 API or RDKit PAINS/structural alert filters, producing a safety/drugability profile for lead prioritization.
- Python: ADMETlab 3.0 REST API via `requests`, `FilterCatalog` for PAINS (RDKit)

Predict absorption, distribution, metabolism, excretion, and toxicity properties.

## ADMETlab 3.0 API

**Goal:** Predict ADMET properties for a batch of compounds using a web API.

**Approach:** Submit SMILES to the ADMETlab 3.0 REST endpoint and parse the returned JSON into a DataFrame of 119 endpoint predictions with uncertainty estimates.

ADMETlab 3.0 provides 119 endpoints with uncertainty estimates.

```python
import requests
import pandas as pd

def predict_admet_batch(smiles_list, api_url='https://admetlab3.scbdd.com/api/predict'):
    '''
    Predict ADMET properties using ADMETlab 3.0 API.

    Note: SwissADME has NO API - it is web-only.
    '''
    payload = {
        'smiles': smiles_list
    }

    response = requests.post(api_url, json=payload)
    response.raise_for_status()

    return pd.DataFrame(response.json())

# Example usage
# smiles = ['CCO', 'c1ccccc1O', 'CC(=O)Oc1ccccc1C(=O)O']
# results = predict_admet_batch(smiles)
```

## Key ADMET Endpoints

| Category | Endpoints | Thresholds |
|----------|-----------|------------|
| Absorption | Caco-2, HIA, Pgp substrate | HIA > 30% |
| Distribution | BBB penetration, PPB, VDss | BBB+: penetrates |
| Metabolism | CYP inhibition (1A2, 2C9, 2C19, 2D6, 3A4) | Inhibitor threshold |
| Excretion | Clearance, Half-life | - |
| Toxicity | hERG, AMES, hepatotoxicity, carcinogenicity | hERG IC50 > 10 μM |

## DeepChem Models

DeepChem supports both PyTorch and TensorFlow backends.

```python
import deepchem as dc

# Load pre-trained toxicity model
tox21_tasks, tox21_datasets, transformers = dc.molnet.load_tox21()
train_dataset, valid_dataset, test_dataset = tox21_datasets

# Featurize new molecules
featurizer = dc.feat.CircularFingerprint(size=1024)
smiles = ['CCO', 'c1ccccc1']
features = featurizer.featurize(smiles)

# Load trained model
model = dc.models.GraphConvModel(
    n_tasks=12,
    mode='classification',
    model_dir='tox21_model'
)

# Predict (after training/loading)
# predictions = model.predict_on_batch(features)
```

## PAINS Filter

**Goal:** Remove pan-assay interference compounds that produce false positives in biological screens.

**Approach:** Build a PAINS FilterCatalog and test each molecule; compounds matching any PAINS pattern are flagged and separated from clean compounds.

```python
from rdkit.Chem.FilterCatalog import FilterCatalog, FilterCatalogParams

def filter_pains(molecules):
    '''
    Filter out PAINS (pan-assay interference compounds).
    These are promiscuous compounds that give false positives in assays.
    '''
    params = FilterCatalogParams()
    params.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS)
    catalog = FilterCatalog(params)

    clean = []
    flagged = []

    for mol in molecules:
        if mol is None:
            continue
        entry = catalog.GetFirstMatch(mol)
        if entry is None:
            clean.append(mol)
        else:
            flagged.append((mol, entry.GetDescription()))

    print(f'Clean: {len(clean)}, PAINS flagged: {len(flagged)}')
    return clean, flagged

# Other filter catalogs available:
# FilterCatalogs.BRENK - Brenk structural alerts
# FilterCatalogs.NIH - NIH structural alerts
# FilterCatalogs.ZINC - ZINC clean leads
```

## Lipinski and Beyond

**Goal:** Assess drug-likeness of a molecule using multiple criteria beyond Lipinski Rule of 5.

**Approach:** Calculate Lipinski properties (MW, LogP, HBD, HBA), count violations, check Veber oral bioavailability criteria (rotatable bonds, TPSA), and compute QED score.

```python
from rdkit import Chem
from rdkit.Chem import Descriptors, Lipinski, QED

def calculate_druglikeness(mol):
    '''
    Calculate multiple drug-likeness criteria.
    '''
    if mol is None:
        return None

    props = {
        # Lipinski Rule of 5
        'MW': Descriptors.MolWt(mol),
        'LogP': Descriptors.MolLogP(mol),
        'HBD': Lipinski.NumHDonors(mol),
        'HBA': Lipinski.NumHAcceptors(mol),

        # Additional properties
        'TPSA': Descriptors.TPSA(mol),
        'RotatableBonds': Lipinski.NumRotatableBonds(mol),
        'AromaticRings': Lipinski.NumAromaticRings(mol),

        # QED (quantitative estimate of drug-likeness)
        # 0-1 scale, > 0.5 generally drug-like
        'QED': QED.qed(mol)
    }

    # Lipinski violations
    violations = 0
    if props['MW'] > 500: violations += 1
    if props['LogP'] > 5: violations += 1
    if props['HBD'] > 5: violations += 1
    if props['HBA'] > 10: violations += 1
    props['LipinskiViolations'] = violations

    # Veber criteria (oral bioavailability)
    # RotatableBonds <= 10, TPSA <= 140
    props['VeberCompliant'] = (props['RotatableBonds'] <= 10 and props['TPSA'] <= 140)

    return props
```

## Prioritization Pipeline

**Goal:** Rank compounds through a multi-stage ADMET filter to identify drug-like leads.

**Approach:** Apply sequential Lipinski, Veber, and QED filters to progressively eliminate compounds that fail drug-likeness criteria.

```python
def prioritize_compounds(molecules):
    '''
    Multi-stage ADMET filtering pipeline.
    '''
    results = []

    for mol in molecules:
        if mol is None:
            continue

        props = calculate_druglikeness(mol)
        if props is None:
            continue

        # Stage 1: Lipinski filter
        if props['LipinskiViolations'] > 1:
            continue

        # Stage 2: Additional filters
        if not props['VeberCompliant']:
            continue

        # Stage 3: QED cutoff
        if props['QED'] < 0.5:
            continue

        results.append((mol, props))

    return results
```

## Related Skills

- molecular-descriptors - Calculate descriptors for ML
- substructure-search - Filter reactive groups
- virtual-screening - Screen after ADMET filtering