bio-molecular-descriptors

Version Compatibility

Reference examples tested with: RDKit 2024.09+, numpy 1.26+, pandas 2.2+, map4 1.1+ (MAP4), mhfp 1.9+. Use mapchiral separately when the stereochemistry-aware MAP4C fingerprint is intended.

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. Fingerprint performance is dataset- and objective-dependent: ECFP4 is a strong drug-like baseline, atom-pair and topological-torsion fingerprints expose longer-range topology, MAP4/MHFP6 target broader chemical-space searches, and 3D conformer-based descriptors are needed 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/subgraph-based	paths and branched subgraphs up to 7 bonds by default	2048	RDKit-native ECFP alternative	Drug-like only; not optimal for scaffold hopping
AtomPair	Pair + topological distance	All atom pairs	2048	Long-range topological similarity	Slower than ECFP; harder to interpret
TopologicalTorsion	4-atom torsion	All TT	2048	Path-pattern similarity	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	map4 library required; slower hash
MHFP6 (MinHash)	MinHash ECFP-like	r=3 (diam 6)	2048	Large-library nearest-neighbor with a compatible MinHash/LSH index	Different distance semantics from folded-bit Tanimoto
Pharm2D	2D pharmacophore	feature pairs/triplets	sparse	Pharmacophore search	Sparse, slower

Decision: For drug-like similarity ranking, start with ECFP4 2048 bit because it is fast and well characterized. MHFP6 outperformed ECFP4 for analog recovery in the benchmark reported by Probst and Reymond (2018), making it a candidate for large, diverse libraries. For scaffold hopping, benchmark ECFP4, AtomPair, TopologicalTorsion, and pharmacophore fingerprints on target-relevant actives and decoys; published comparisons do not support a universal AtomPair advantage (Gardiner et al. 2011; Riniker & Landrum 2013).

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 rdFingerprintGenerator

mol = Chem.MolFromSmiles('CCO')

morgan = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
ecfp4_bit = morgan.GetFingerprint(mol)
ecfp4_count = morgan.GetCountFingerprint(mol)
ecfp4_sparse = morgan.GetSparseCountFingerprint(mol)

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 environments but increases collisions at fixed nBits. ECFP4 2048 is a common baseline (Rogers & Hahn 2010; Wu et al. 2018). O'Boyle and Sayle (2016) showed that increasing folded-fingerprint length can improve virtual-screening performance, but they did not establish a universal 4096-bit setting or a 1-5% collision rate. Measure collision occupancy and model performance for the dataset; increase nBits or use an unhashed sparse representation when needed.

FCFP vs ECFP

FCFP (Functional-Class) uses RDKit's Morgan feature invariants (donor, acceptor, aromatic, halogen, basic, and acidic) instead of atom identity. Hydrophobe is a family in BaseFeatures.fdef, but it is not one of the default Morgan feature-invariant classes. FCFP trades atom-specificity for functional-equivalence.

ecfp_generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
feature_invariants = rdFingerprintGenerator.GetMorganFeatureAtomInvGen()
fcfp_generator = rdFingerprintGenerator.GetMorganGenerator(
    radius=2, fpSize=2048, atomInvariantsGenerator=feature_invariants)
ecfp4 = ecfp_generator.GetFingerprint(mol)
fcfp4 = fcfp_generator.GetFingerprint(mol)

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.

3D Descriptors and Conformer Dependence

Conformer-dependent descriptors (asphericity, eccentricity, principal moments of inertia, RDF) require a generated 3D structure. A single conformer may be unrepresentative when the molecule is flexible; measure descriptor variation across a conformer ensemble when the downstream conclusion depends on 3D shape.

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
conf_ids = AllChem.EmbedMultipleConfs(mol, numConfs=20, params=params)
if not conf_ids:
    raise RuntimeError('ETKDGv3 failed to generate any conformers')
if not AllChem.MMFFHasAllMoleculeParams(mol):
    raise ValueError('MMFF94 parameters are unavailable for this molecule')
optimization_results = AllChem.MMFFOptimizeMoleculeConfs(mol)
if any(status != 0 for status, _ in optimization_results):
    raise RuntimeError('MMFF94 optimization did not converge for every conformer')

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

Decision: For QSAR / ML, choose and document the conformer count using a convergence check on representative molecules. Report the aggregation rule, such as a simple mean or a Boltzmann-weighted average, and the energy model used for any weights.

Partial Charge Methods

Method	Software	Cost	Accuracy	Use for
Gasteiger-Marsili	RDKit, Open Babel	Fast	Empirical, rough	Charge-aware preparation or models that explicitly require Gasteiger charges; Vina/Vinardo scoring itself does not require assigned atom charges
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	Restrained fit to a quantum-mechanical ESP; protocol-specific	Force-field workflows parameterized for that RESP protocol
OpenFF Recharge	openff-recharge	Workflow-dependent	Framework for generating/retrieving QC ESP data and fitting library charges, BCCs, RESP charges, or virtual sites	Developing or evaluating charge models; it is not one charge-assignment method

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 molecules, natural products, peptides, and metabolites, compare ECFP4 with MAP4 or MHFP6 on task-relevant retrieval benchmarks. MAP4 and MHFP6 use MinHash with atom-pair or circular-substructure shingles, but no universal pairwise-similarity range establishes that ECFP4 is saturated for every mixed library.

from mhfp.encoder import MHFPEncoder

encoder = MHFPEncoder(2048)
mhfp6 = encoder.encode_mol(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	Descriptive; higher sp3 character was associated with clinical progression by Lovering et al. (2009), without a universal cutoff
QED	QED.qed	0-1	Higher is more similar to the reference property distributions; a project may use >=0.5 as a triage heuristic
SAscore	sascorer.calculateScore (external)	1-10	Lower is easier by the model; project cutoffs such as <=4 or >6 require dataset calibration

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, C>4, heteroatoms>1, RotBonds<=15, HBD<=5, HBA<=10	Muegge 2001
Lead-like	MW<=350, LogP<=3	Teague 1999
Fragment Ro3	MW<=300, LogP<=3, HBD<=3, HBA<=3, RotBonds<=3, TPSA<=60 A^2	Congreve 2003
Pfizer CNS MPO	Six desirability functions: ClogP, ClogD, MW, TPSA, HBD, and pKa	Wager 2010

Use case: Treat Ro5 and Veber criteria as risk indicators rather than universal hard cutoffs. Doak et al. (2014) analyze orally bioavailable drugs and candidates beyond the Rule of 5, but do not support the claim that approximately 30% of marketed oral drugs violate at least one rule. For CNS prioritization, implement the six-property Wager MPO desirability score rather than replacing it with three hard thresholds.

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 summarizes desirability functions derived from property distributions of marketed oral drugs; it is not a supervised predictor trained specifically on FDA-approved drugs. It can under-rank fragment-like or natural-product-like molecules, so do not use it as the sole filter for those 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
Count-vector similarity differs from bit-vector similarity	Count multiplicities change the generalized Tanimoto calculation	RDKit supports Tanimoto on sparse count vectors; record the vector type and do not compare its threshold directly with a folded-bit threshold
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. 50:742-754 (2010) -- ECFP fingerprints. https://doi.org/10.1021/ci100050t
• Probst & Reymond, J. Cheminformatics 10:66 (2018) -- MHFP6 fingerprint. https://doi.org/10.1186/s13321-018-0321-8
• Capecchi et al., J. Cheminformatics 12:43 (2020) -- MAP4 fingerprint. https://doi.org/10.1186/s13321-020-00445-4
• MAP4, official package -- installation and current Python interface. https://pypi.org/project/map4/
• OpenFF Recharge, official documentation -- supported charge-model generation and fitting workflows. https://docs.openforcefield.org/projects/recharge/en/stable/
• AutoDock Vina, official documentation -- Vina/Vinardo charge semantics. https://autodock-vina.readthedocs.io/en/stable/
• Gardiner et al., Future Med. Chem. 3:405-414 (2011) -- scaffold-hopping fingerprint comparison. https://doi.org/10.4155/fmc.11.4
• Riniker & Landrum, J. Cheminformatics 5:26 (2013) -- fingerprint benchmarking. https://doi.org/10.1186/1758-2946-5-26
• O'Boyle & Sayle, J. Cheminformatics 8:36 (2016) -- fingerprint folding and virtual-screening performance. https://doi.org/10.1186/s13321-016-0148-0
• Wu et al., Chem. Sci. 9:513-530 (2018) -- MoleculeNet benchmarks. https://doi.org/10.1039/C7SC02664A
• Bickerton et al., Nat. Chem. 4:90-98 (2012) -- QED weighted drug-likeness. https://doi.org/10.1038/nchem.1243
• Lipinski et al., Adv. Drug Deliv. Rev. 23:3-25 (1997) -- Rule of 5. https://doi.org/10.1016/S0169-409X(96)00423-1
• Veber et al., J. Med. Chem. 45:2615-2623 (2002) -- oral bioavailability criteria. https://doi.org/10.1021/jm020017n
• Ghose et al., J. Comb. Chem. 1:55-68 (1999) -- physicochemical property ranges. https://doi.org/10.1021/cc9800071
• Egan et al., J. Med. Chem. 43:3867-3877 (2000) -- absorption model. https://doi.org/10.1021/jm000292e
• Muegge et al., J. Med. Chem. 44:1841-1846 (2001) -- drug-like property filters. https://doi.org/10.1021/jm015507e
• Teague et al., Angew. Chem. Int. Ed. 38:3743-3748 (1999) -- lead-like libraries. https://doi.org/10.1002/%28SICI%291521-3773%2819991216%2938%3A24%3C3743%3A%3AAID-ANIE3743%3E3.0.CO%3B2-U
• Congreve et al., Drug Discov. Today 8:876-877 (2003) -- Rule of 3. https://doi.org/10.1016/S1359-6446(03)02831-9
• Lovering et al., J. Med. Chem. 52:6752-6756 (2009) -- fraction sp3 and clinical progression. https://doi.org/10.1021/jm901241e
• Ritchie & Macdonald, Drug Discov. Today 14:1011-1020 (2009) -- aromatic ring count and developability. https://doi.org/10.1016/j.drudis.2009.07.014
• Ertl & Schuffenhauer, J. Cheminformatics 1:8 (2009) -- synthetic accessibility score. https://doi.org/10.1186/1758-2946-1-8
• Wager et al., ACS Chem. Neurosci. 1:435-449 (2010) -- CNS multiparameter optimization. https://doi.org/10.1021/cn100008c
• Doak et al., Chem. Biol. 21:1115-1142 (2014) -- orally bioavailable drugs beyond the Rule of 5. https://doi.org/10.1016/j.chembiol.2014.08.013

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
• machine-learning/biomarker-discovery - ML on molecular features