molecular-descriptors

Version Compatibility

Reference examples tested with: RDKit 2024.09+, numpy 1.26+, pandas 2.2+, mapchiral 0.1+ (MAP4), mhfp 1.9+, molfeat 0.10+.

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.

Molecular Descriptors

Featurize molecules for similarity search, QSAR, virtual screening, or ML. The fingerprint or descriptor choice is chemotype-aware: ECFP4 dominates drug-like organic similarity, AtomPair and TopologicalTorsion outperform for scaffold hopping, MAP4/MHFP6 win on metabolomics-scale chemical diversity, and 3D conformer-based descriptors are essential when shape and stereochemistry matter.

For canonicalization before featurization, see chemoinformatics/molecular-standardization. For 3D-only descriptors, see chemoinformatics/conformer-generation.

Fingerprint Taxonomy

Fingerprint	Type	Radius/Path	Bits	Use case	Fails when
Morgan (ECFP)	Circular	r=2 (ECFP4), r=3 (ECFP6)	2048 typical	Drug-like similarity, ML default	Loses long-range topology; bit collisions at low nBits
FCFP	Functional Morgan	r=2 default	2048	Pharmacophore-aware similarity	Same caveats as ECFP; less specific
MACCS	Substructure key	166 fixed bits	167	Quick fingerprint, drug-likeness	Too sparse for large diverse libraries
RDKit FP	Path-based	linear paths up to 7 atoms	2048	RDKit-native ECFP alternative	Drug-like only; not optimal for scaffold hopping
AtomPair	Pair + topological distance	All atom pairs	2048	Scaffold hopping; flexible mol	Slower than ECFP; harder to interpret
TopologicalTorsion	4-atom torsion	All TT	2048	Scaffold hopping; less hit-rate	Like AP, slower than ECFP
Avalon	Substructure + atom pairs	Mixed	512/1024	Fast similarity	Less standard; older
MAP4 (MinHashed atom-pair)	MinHash atom-pair	r=1,2	1024/2048	Biological + metabolite diversity	Library required (mapchiral); slower hash
MHFP6 (MinHash)	MinHash ECFP-like	r=3 (diam 6)	2048	Big-data nearest-neighbor (Annoy)	Different distance (Jaccard on MinHash)
Pharm2D	2D pharmacophore	feature pairs/triplets	sparse	Pharmacophore search	Sparse, slower

Decision: For drug-like similarity ranking, use ECFP4 2048 bit; established baseline, fast, well-understood. For diverse libraries (>1M compounds, metabolomics, peptides), MHFP6 outperforms ECFP4 on analog recovery (Probst & Reymond 2018). For scaffold hopping, AtomPair beats ECFP4 on retrospective benchmarks but loses on retrospective single-target.

Bit vs Count Vectors

Form	Use	Library impact
Bit (0/1)	Tanimoto similarity, BulkTanimotoSimilarity, RDKit fingerprint folding	Standard for similarity
Count (integer)	Some ML methods, RF on counts, neural fingerprints	Loses bit-level fast operations; richer signal
Sparse (dict)	Direct chemical interpretation (which fragments at which atoms)	Use for SHAP / atomic attribution

from rdkit import Chem
from rdkit.Chem import AllChem

mol = Chem.MolFromSmiles('CCO')

ecfp4_bit = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
ecfp4_count = AllChem.GetHashedMorganFingerprint(mol, radius=2, nBits=2048)
ecfp4_sparse = AllChem.GetMorganFingerprint(mol, radius=2)

Morgan / ECFP Radius Math

ECFP-X notation: X is the diameter in bonds. RDKit's radius parameter is half of X.

Notation	RDKit radius	Diameter	Captures
ECFP0	0	0	Atom identity only
ECFP2	1	2	Atom + immediate neighbors
ECFP4	2	4	Atom + 2-bond environment
ECFP6	3	6	Atom + 3-bond environment

Trade-off: Larger radius captures more specific local environment but inflates bit-collision rate at fixed nBits. For QSAR with <10k compounds, ECFP4 2048 is the established default (Rogers & Hahn 2010; MoleculeNet benchmarks Wu 2018). For large libraries (>1M), use nBits=4096 or unhashed sparse representation to reduce ~1-5% bit-collision rate (O'Boyle 2016).

FCFP vs ECFP

FCFP (Functional-Class) uses atom invariants based on pharmacophore role (donor, acceptor, hydrophobe, aromatic, halogen, basic, acidic) instead of atom identity. Trades atom-specificity for functional-equivalence.

ecfp4 = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, useFeatures=False)
fcfp4 = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, useFeatures=True)

When to use FCFP4: Scaffold-hopping campaigns, pharmacophore-driven similarity, cross-target activity prediction.

When to use ECFP4: Within-series QSAR, lead optimization, when chemotype identity matters.

molfeat: Pluggable Featurization

molfeat (Datamol/Molecular AI) wraps RDKit, Mordred, Vina, and hand-crafted transformers behind a uniform Transformer interface. It is the right choice when you need to swap featurizations programmatically (e.g. for a benchmark or a notebook exploration) rather than hardcoding the RDKit calls.

from molfeat.trans.fp import FPVecTransformer

ecfp4 = FPVecTransformer(kind='ecfp', length=2048, radius=2)
fcfp4 = FPVecTransformer(kind='fcfp', length=2048, radius=2)
ap = FPVecTransformer(kind='ap', length=2048)
mhfp6 = FPVecTransformer(kind='mhfp', length=2048, radius=3)

# X is a numpy array, y is a list of mols or SMILES
X = ecfp4(list_of_mols)

For large fingerprints (MHFP6, AtomPair) or large libraries, molfeat avoids the boilerplate of building fingerprints one molecule at a time.

3D Descriptors and Conformer Dependence

Conformer-dependent descriptors (asphericity, eccentricity, principal moments of inertia, RDF) require a generated 3D structure. A single conformer is rarely sufficient: descriptor variance across the conformer ensemble can exceed the descriptor signal.

Goal: Compute 3D shape descriptors over a conformer ensemble rather than from a single (possibly unrepresentative) conformer.

Approach: Add explicit hydrogens, embed N conformers with ETKDGv3, MMFF-optimize them all, then evaluate the descriptor across each conformer for downstream averaging.

from rdkit.Chem import AllChem, Descriptors3D

mol = Chem.MolFromSmiles('CCCCO')
mol = Chem.AddHs(mol)

params = AllChem.ETKDGv3()
params.randomSeed = 42
n = AllChem.EmbedMultipleConfs(mol, numConfs=20, params=params)
AllChem.MMFFOptimizeMoleculeConfs(mol)

asphericities = [Descriptors3D.Asphericity(mol, confId=c) for c in range(n)]

Decision: For QSAR / ML, compute over a conformer ensemble (n=20-100) and report mean or Boltzmann-weighted average. Single-conformer 3D descriptors are unreliable.

Partial Charge Methods

Method	Software	Cost	Accuracy	Use for
Gasteiger-Marsili	RDKit, Open Babel	<0.1s/mol	Empirical, rough	AutoDock Vina, fast screening
MMFF94	RDKit	0.1s/mol	Force-field consistent	MMFF energy, conformer ranking
AM1-BCC	antechamber (AmberTools)	~10s/mol	Semi-empirical	MD setup, FEP, GAFF
RESP	psi4, Gaussian	minutes/mol	DFT ESP-fitted	High-accuracy MD, FEP
OpenFF Recharge	openff-recharge	seconds	DFT-derived but cached	OpenFF / SAGE setup
ABCG2	Open Babel	<1s	Improved empirical	Modern Vina, AutoDock-GPU

from rdkit.Chem import AllChem

AllChem.ComputeGasteigerCharges(mol)
for atom in mol.GetAtoms():
    print(atom.GetIdx(), atom.GetPropsAsDict().get('_GasteigerCharge', None))

Critical: Charge method must match downstream. Gasteiger charges in an AMBER MD run violate the assumptions of the protein force field.

MAP4 and MHFP6 for Diverse Libraries

For libraries spanning drug-like + natural products + peptides + metabolites, ECFP4 saturates Tanimoto similarity (most pairs report 0.1-0.3, hard to rank). MAP4 and MHFP6 use MinHash + atom-pair / circular substructures and discriminate better.

from mhfp.encoder import MHFPEncoder

encoder = MHFPEncoder
mhfp6 = encoder.encode(mol, radius=3)

MHFP6 distance is Jaccard on MinHash, not standard Tanimoto. Use MHFPEncoder.distance(fp1, fp2).

Physicochemical Descriptors

Descriptor	Source	Range	Drug-like cutoff
MolWt	RDKit Descriptors.MolWt	~50-2000 Da	<=500 (Lipinski)
MolLogP (Crippen)	RDKit Descriptors.MolLogP	-5 to 8	<=5 (Lipinski)
HBD	Lipinski.NumHDonors	0-10	<=5 (Lipinski)
HBA	Lipinski.NumHAcceptors	0-15	<=10 (Lipinski)
TPSA	Descriptors.TPSA (Ertl)	0-200 A^2	<=140 (Veber oral); <=90 (BBB+)
RotBonds	Lipinski.NumRotatableBonds	0-15	<=10 (Veber)
AromaticRings	Lipinski.NumAromaticRings	0-6	<=3-4 (Ritchie-Macdonald aromatic ring count)
HeavyAtoms	Descriptors.HeavyAtomCount	<=50 (lead-like)
FractionCSP3	Descriptors.FractionCSP3	0-1	>=0.25 (Lovering 2009 escape-from-flatland)
QED	QED.qed	0-1	>=0.5 generally drug-like
SAscore	sascorer.calculateScore (external)	1-10	<=4 acceptable; >6 hard to synth

Goal: Compute a standard physicochemical descriptor panel for drug-likeness filtering and QSAR features.

Approach: Combine RDKit Descriptors, Lipinski, and QED calls into a single dict so the caller gets MW, LogP, HBD/HBA, TPSA, rotatable bonds, aromatic rings, fraction sp3, and QED in one pass.

from rdkit.Chem import Descriptors, Lipinski, QED

def physchem(mol):
    return {
        'MolWt': Descriptors.MolWt(mol),
        'MolLogP': Descriptors.MolLogP(mol),
        'HBD': Lipinski.NumHDonors(mol),
        'HBA': Lipinski.NumHAcceptors(mol),
        'TPSA': Descriptors.TPSA(mol),
        'RotBonds': Lipinski.NumRotatableBonds(mol),
        'AromRings': Lipinski.NumAromaticRings(mol),
        'FractionCSP3': Descriptors.FractionCSP3(mol),
        'QED': QED.qed(mol),
    }

Drug-Likeness Rule Sets

Rule	Constraints	Source
Lipinski Ro5	MW<=500, LogP<=5, HBD<=5, HBA<=10	Lipinski 1997
Veber	RotBonds<=10, TPSA<=140	Veber 2002 (oral)
Ghose	160<=MW<=480, -0.4<=LogP<=5.6, 40<=MR<=130, 20<=atoms<=70	Ghose 1999
Egan	LogP<=5.88, TPSA<=131.6	Egan 2000
Muegge	200<=MW<=600, -2<=LogP<=5, TPSA<=150, rings<=7	Muegge 2001
Lead-like	MW<=350, LogP<=3	Teague 1999
Fragment Ro3	MW<=300, LogP<=3, HBD<=3, HBA<=3, RotBonds<=3	Congreve 2003
BBB+ Pfizer CNS	TPSA<=90, MW<=500, HBD<=3	Wager 2010

Use case: Apply Ro5/Veber as a screening filter, not a hard cutoff. ~30% of marketed oral drugs violate at least one Ro5 rule (analyzed by Doak 2014). For oncology indications, Ro5 deviation is common and acceptable.

QED (Weighted Drug-Likeness)

QED (Bickerton 2012) is a single-number drug-likeness measure (0-1) combining 8 properties (MW, LogP, HBD, HBA, PSA, RotBonds, AromaticRings, structural alerts) via desirability functions.

Caveat: QED is trained on FDA-approved drugs; it under-rates fragment-like and natural-product-like molecules. Do not use as the sole drug-likeness filter for fragment screens or natural-product libraries.

Common Errors

Symptom	Cause	Fix
Fingerprint changes between runs	Random seed not set for canonicalization	RDKit Morgan is deterministic; check if input differs (stereo, charges)
MACCS bit count != 166	RDKit MACCS returns 167 bits (bit 0 unused)	Slice [1:] if comparing to literature 166-bit
Crippen LogP differs from XLogP	Different model	Use Descriptors.MolLogP for Crippen; XLogP3 requires external lib
3D descriptor differs between calls	Different conformer	Set confId=0 explicitly; or average over ensemble
QED returns nan	Charged species or non-standard atom	Standardize (uncharge) before QED
Tanimoto on count vector wrong	TanimotoSimilarity expects bit vector	Use Hamming or weighted Tanimoto for counts
MolWt off by ~1 from PubChem	Implicit H counted differently	Use Descriptors.ExactMolWt for monoisotopic; PubChem reports average

References

• Rogers & Hahn, J. Chem. Inf. Model. 50:742 -- ECFP / Morgan fingerprints.
• Probst & Reymond, J. Cheminformatics 10:66 -- MHFP6 fingerprint.
• Capecchi et al., J. Cheminformatics 12:43 -- MAP4 fingerprint.
• Bickerton et al., Nat. Chem. 4:90 -- QED weighted drug-likeness.
• Lipinski et al., Adv. Drug Deliv. Rev. 23:3 -- Rule of 5.
• Veber et al., J. Med. Chem. 45:2615 -- Oral bioavailability rules.
• Lovering et al., J. Med. Chem. 52:6752 -- Fraction sp3 / escape from flatland.

Related Skills

• chemoinformatics/molecular-io - Parse molecules before featurization
• chemoinformatics/molecular-standardization - Canonicalize before fingerprinting
• chemoinformatics/conformer-generation - Generate 3D for conformer-dependent descriptors
• chemoinformatics/similarity-searching - Use fingerprints for similarity ranking
• chemoinformatics/qsar-modeling - ML using these descriptors as features
• chemoinformatics/admet-prediction - Filter by drug-likeness criteria"