bio-scaffold-analysis

name	bio-scaffold-analysis
description	Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.
tool_type	python
primary_tool	RDKit

Version Compatibility

Reference examples tested with: RDKit 2024.09+, mmpdb 3.1+, scikit-learn 1.4+, datamol 0.12+.

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.

Scaffold Analysis

Analyze chemical libraries by their underlying scaffolds. Bemis-Murcko (1996) is the canonical scaffold decomposition: ring systems + linkers, with all R-groups stripped. Generic framework + cyclic skeleton are progressively-more-abstract views. Scaffold analysis underpins QSAR train/test splits (preventing data leakage), library diversity assessment, chemotype clustering, R-group decomposition for SAR modeling, and matched molecular pair analysis (MMPA). The choice of scaffold representation determines whether two compounds are "the same series" -- a critical decision for medicinal chemistry workflows.

For reaction-based enumeration and Free-Wilson, see chemoinformatics/reaction-enumeration. For scaffold-hopping via fingerprints, see chemoinformatics/similarity-searching. For 3D shape-based scaffold hopping, see chemoinformatics/shape-similarity.

Scaffold Representation Taxonomy

Representation	Origin	Definition	Use case	Fails when
Bemis-Murcko scaffold	Bemis & Murcko 1996	Ring systems + linkers, R-groups stripped	Default chemotype identifier	Linear molecules (no rings) → empty scaffold
Generic framework	Bemis & Murcko 1996	Bemis-Murcko with all atoms set to C, all bonds single	Topology comparison	Loses heteroatom info
Cyclic skeleton (CSK)	RDKit	Ring atoms only, all C, all single	Pure ring-topology view	Loses linker info
Murcko atom set	RDKit GetScaffoldForMol(returnMol=False)	Atom indices	Programmatic operations	Not a SMILES
Sphynx fingerprints	Maggiora 2020	Scaffold + connection signature	Cross-target scaffold hopping	Specialty use

from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold

def all_scaffold_views(smi):
    mol = Chem.MolFromSmiles(smi)
    bm = MurckoScaffold.GetScaffoldForMol(mol)
    bm_smi = Chem.MolToSmiles(bm)

    generic = MurckoScaffold.MakeScaffoldGeneric(bm)
    generic_smi = Chem.MolToSmiles(generic)

    return {
        'bemis_murcko': bm_smi,
        'generic_framework': generic_smi,
    }

Example: Cc1ccc(C(=O)NCC2CCCC2)cc1 → Bemis-Murcko c1ccc(C(=O)NCC2CCCC2)cc1; generic C1CCC(C(C)NCC2CCCC2)CC1.

Library Chemotype Clustering

Goal: Group compounds by shared Bemis-Murcko scaffold.

Approach: Compute scaffold for each compound; group by scaffold SMILES.

from collections import defaultdict

def scaffold_clusters(smiles_list):
    clusters = defaultdict(list)
    for smi in smiles_list:
        mol = Chem.MolFromSmiles(smi)
        if mol is None:
            continue
        scaffold = MurckoScaffold.GetScaffoldForMol(mol)
        scaffold_smi = Chem.MolToSmiles(scaffold)
        clusters[scaffold_smi].append(smi)
    return clusters

Output: dict {scaffold_smiles: [compound_smiles, ...]}. Cluster sizes inform library diversity.

Bemis-Murcko Scaffold Split (ML)

For QSAR / ML, random train/test split causes data leakage: compounds from the same chemotype (analogs in same series) end up in both. Bemis-Murcko split puts entire scaffolds in train or test, never both.

from rdkit.Chem.Scaffolds import MurckoScaffold

def scaffold_split(df, smiles_col='smiles', train_frac=0.8, seed=42):
    import random
    random.seed(seed)

    scaffolds = defaultdict(list)
    for i, row in df.iterrows():
        mol = Chem.MolFromSmiles(row[smiles_col])
        if mol is None:
            continue
        scaff = Chem.MolToSmiles(MurckoScaffold.GetScaffoldForMol(mol))
        scaffolds[scaff].append(i)

    scaffold_sets = sorted(scaffolds.values(), key=lambda x: len(x), reverse=True)

    n_total = sum(len(s) for s in scaffold_sets)
    n_train = int(n_total * train_frac)
    train_idx = []
    test_idx = []
    for scaff_set in scaffold_sets:
        if len(train_idx) + len(scaff_set) <= n_train:
            train_idx.extend(scaff_set)
        else:
            test_idx.extend(scaff_set)

    return df.iloc[train_idx], df.iloc[test_idx]

Effect on benchmark metrics: Random split AUC 0.95; Bemis-Murcko split AUC 0.75-0.85 typical. The gap measures true generalization vs memorization.

Caveat: Bemis-Murcko split is one scaffold-split; for production ML, consider time split (newer compounds in test) or activity-cliff-balanced split.

Class-imbalanced datasets: For binary outcomes (e.g. hERG blocker, AMES mutagen) with class imbalance, scaffold-only assignment can yield test sets with skewed class distribution and unreliable metrics. Use stratified scaffold split: cluster scaffolds, then assign clusters preserving class balance in train + test. Available as chemprop --split scaffold_balanced (does class-aware scaffold partitioning); for custom workflows, combine sklearn.model_selection.StratifiedKFold with scaffold-grouped folds (GroupKFold then StratifiedShuffleSplit on residual).

R-Group Decomposition

Goal: Given a defined scaffold and a set of analog compounds, extract the R-group at each numbered attachment point into a tabular SAR matrix.

from rdkit.Chem import rdRGroupDecomposition as rgd

def decompose_series(compounds, scaffold_smiles_with_R):
    scaffold = Chem.MolFromSmiles(scaffold_smiles_with_R)
    mols = [Chem.MolFromSmiles(s) for s in compounds]
    decomp, _ = rgd.RGroupDecompose([scaffold], mols, asSmiles=True)
    return decomp

scaffold = 'c1ccc(C(=O)N[*:1])cc1-[*:2]'
compounds = ['c1ccc(C(=O)NCC)cc1F', 'c1ccc(C(=O)NCCC)cc1Cl']
table = decompose_series(compounds, scaffold)

Output: list of {'Core': scaffold, 'R1': r1_smiles, 'R2': r2_smiles} dicts. Used for Free-Wilson analysis (see reaction-enumeration skill).

Matched Molecular Pair Analysis (MMPA) via mmpdb

Goal: Mine a SAR dataset for substructure transformations and their associated activity changes.

Approach: Fragment all compounds into core + variable side; index pairs differing by one transformation; report delta(activity) per transformation.

mmpdb fragment data.smi -o data.fragments
mmpdb index data.fragments -o data.mmpdb
mmpdb transform --smiles 'COc1ccccc1' --property pIC50 data.mmpdb

Output: ranked transformations with delta(pIC50), N pairs, confidence.

Confidence interpretation:

• N >= 50, |delta| > 0.5 → reliable rule
• N = 10-50, |delta| > 1.0 → suggestive
• N < 10 → anecdotal

Context-Based MMPA

Classical MMPA: "Me → F always +0.5 log units." Context-based MMPA: "Me → F adjacent to amide is +0.5; Me → F adjacent to ester is -0.1."

Awale et al. 2024 showed context-conditioned transformations have 60% higher predictive accuracy. mmpdb supports context via --context flag for pre-defined contexts; for arbitrary contexts, custom analysis.

Scaffold Hopping

Goal: Find compounds with different scaffold but similar 3D shape / pharmacophore / activity.

Method	Approach	Tools
2D similarity with FCFP4	Functional-class fingerprint Tanimoto	similarity-searching skill
3D shape (ROCS)	Tanimoto on shape + color volumes	shape-similarity skill
Pharmacophore	Common pharmacophore features	pharmacophore-modeling skill
Maximum Common Substructure (MCS)	Largest shared substructure	similarity-searching skill (rdFMCS)
Deep scaffold hopping	Multi-modal transformer NN	DeepScaffoldHop (Devereux 2024)

For systematic scaffold-hop discovery, combine:

1. Find target's bioactive series
2. Compute 3D pharmacophore from bound conformer
3. ROCS / pharmacophore search against vendor catalogs
4. Filter to compounds with Bemis-Murcko scaffold NOT in training data

Series Detection

Goal: Identify "analog series" within a library -- compounds sharing a scaffold + co-varying R-groups.

def detect_series(smiles_list, min_size=3):
    clusters = scaffold_clusters(smiles_list)
    series = {scaff: cmpds for scaff, cmpds in clusters.items()
              if len(cmpds) >= min_size}
    return series

For a 10k-compound library, expect 100-500 series of size >= 3. Series are units for SAR modeling.

Per-Tool Failure Modes

Bemis-Murcko -- linear molecule yields empty

Trigger: Compound has no rings (e.g., fatty acid, simple amine).

Mechanism: Bemis-Murcko strips R-groups; no rings = nothing remains.

Symptom: Scaffold is empty string; molecules cluster together as "no scaffold".

Fix: For linear-rich libraries, augment with linear chain length / functional group features.

Bemis-Murcko -- spiro / bridged ring confusion

Trigger: Compound has spiro or bridged ring system.

Mechanism: All ring atoms included; result is the entire ring system without R-groups.

Symptom: Apparently different drugs share a "scaffold" because of common spiro center.

Fix: Validate visually; use generic framework for topology-only comparison.

Generic framework -- loses heteroatom info

Trigger: Distinguishing pyridine vs benzene scaffolds.

Mechanism: MakeScaffoldGeneric sets all atoms to C.

Symptom: Pyridine and benzene scaffolds reported as identical.

Fix: Use Bemis-Murcko (heteroatoms preserved); generic framework for topology only.

Scaffold split -- imbalanced classes

Trigger: Library has many singletons + few large scaffolds.

Mechanism: Large scaffolds dominate; greedy assignment puts them in train.

Symptom: Test set is mostly singleton scaffolds; metrics misleading.

Fix: Use stratified scaffold split (balance test classes); or scaffold-balanced cross-validation.

MMPA -- low pair count for novel transformations

Trigger: Transformation rare in dataset.

Mechanism: Need enough pairs to estimate delta(activity).

Symptom: Transformation reports N=2 with very large delta.

Fix: Filter N >= 10; supplement with vendor catalogs (Enamine + ChEMBL).

R-group decomposition -- ambiguous match

Trigger: Multiple positions in scaffold could match same R-group.

Mechanism: RGroupDecompose returns first match; not necessarily the "intended" one.

Symptom: R1/R2 columns mixed up.

Fix: Specify scaffold with explicit [*:1] and [*:2] placeholders at desired positions.

Reconciliation: Scaffold Definition Disagreements

Concept	Definition A	Definition B	Pick which
Bemis-Murcko scaffold	Atoms in rings + linkers	Same	RDKit default
Generic framework	All C, all single bonds	All C, original bonds	RDKit makeAtomsGeneric=False for variant
Cyclic skeleton	Only ring atoms	Only ring atoms, generic	RDKit specific
"Series"	Same Bemis-Murcko	Tanimoto > 0.8 + same MW	Bemis-Murcko for SAR; Tanimoto for screening

For ML splits: Bemis-Murcko. For library diversity: Bemis-Murcko + cluster size. For series detection: Bemis-Murcko + R-group decomposition.

Common Errors

Symptom	Cause	Fix
GetScaffoldForMol returns mol with extra atoms	Linker definition includes 2-bond span	Use BMScaffoldNetwork to control linker depth
Singleton scaffolds dominate library	Aggressive standardization	Check for tautomer-induced scaffold variation; canonicalize first
R-group decomposition empty	Mol doesn't match scaffold	Use FMCS to find actual shared core
mmpdb missing transformations	Cores too restrictive	Try smaller core requirement
Scaffold split gives all to train	Few scaffolds; large clusters	Add singleton-spread strategy; use Murcko-and-Linker variant
Generic framework same for different drugs	Stripped heteroatom info	Use Bemis-Murcko (preserves heteroatoms)
MakeScaffoldGeneric error	RDKit version issue	RDKit 2024.09+ uses Chem.Scaffolds.MurckoScaffold

References

• Bemis & Murcko, J. Med. Chem. 39:2887 (1996) -- original scaffold framework.
• Hu et al., J. Chem. Inf. Model. 57:171 (2017) -- modern scaffold hopping review.
• Hussain & Rea, J. Chem. Inf. Model. 50:339 (2010) -- MMPA core method.
• Awale et al., J. Cheminformatics 17:23 (2024) -- context-based MMPA on CYP1A2.
• Devereux et al., J. Cheminformatics 16:18 (2024) -- deep scaffold hopping with transformers.
• Yang et al., J. Chem. Inf. Model. 59:3370 (2019) -- chemprop scaffold split for QSAR.

Related Skills

• chemoinformatics/molecular-io - Parse compounds
• chemoinformatics/molecular-standardization - Standardize before scaffold extraction
• chemoinformatics/reaction-enumeration - Free-Wilson analysis on R-decomposition
• chemoinformatics/similarity-searching - 2D scaffold-hopping (FCFP4, AtomPair)
• chemoinformatics/shape-similarity - 3D scaffold-hopping
• chemoinformatics/qsar-modeling - Mandatory scaffold split for QSAR
• chemoinformatics/generative-design - Scaffold-decoration generative tasks