cheminformatics

Cheminformatics (RDKit) — Pipeline Skill (Thin Scaffold)

Overview

Adds fingerprint selection logic, filter cascade ordering, and version-specific gotchas for RDKit workflows. The agent already knows RDKit syntax — this skill prevents parameter mismatches and deprecated API usage.

Usage

• Activate when filtering compound libraries (Lipinski/Veber/PAINS)
• Activate for fingerprint similarity search or clustering
• Activate for matched molecular pair (MMP) analysis with mmpdb

Core Concepts

Filter cascade order: Lipinski → Veber → PAINS → similarity (cheapest first, most expensive last).

Fingerprint API: Use rdFingerprintGenerator module exclusively (since RDKit 2024.03). The AllChem.GetMorganFingerprintAsBitVect() path is deprecated and will be removed.

SMILES validation: Always parse with sanitize=False then explicit SanitizeMol() in try/except. Default MolFromSmiles silently returns None on partial failures.

Decision Logic

Fingerprint Selection

Goal	Fingerprint	Parameters	Tanimoto threshold
General similarity	Morgan (ECFP4)	radius=2, fpSize=2048	≥ 0.7
High-specificity matching	Morgan (ECFP6)	radius=3, fpSize=4096	≥ 0.6
Substructure screening	MACCS 166 keys	—	≥ 0.8
Pharmacophore similarity	FCFP	radius=2, GetMorganFeatureAtomInvGen()	≥ 0.6
Large-scale clustering	Morgan	radius=2, fpSize=1024	Speed over precision

Filter Thresholds

Filter	Rule	Threshold	Notes
Lipinski MW	≤ 500 Da	Allow ≤1 violation total	Not binary pass/fail
Lipinski LogP	≤ 5	—	—
Lipinski HBD	≤ 5	—	—
Lipinski HBA	≤ 10	—	—
Veber TPSA	≤ 140 Ų	Hard cutoff	—
Veber RotBonds	≤ 10	Hard cutoff	—
PAINS	A+B+C catalogs	HTS triage only	Don't apply to advanced compounds

MMP Analysis Decision

Scenario	Tool	Key parameter
SAR from compound pairs	mmpdb fragment → index → transform	--min-pairs 3
Property prediction	mmpdb predict	Requires indexed DB with property data
Core identification	rdFMCS.FindMCS()	ringMatchesRingOnly=True, completeRingsOnly=True
Large libraries (>10K)	mmpdb CLI	--max-variable-size 10 to limit fragmentation

Critical Parameters

Parameter	Value	When to change
Morgan radius	2 (ECFP4)	3 for higher specificity
fpSize	2048	1024 for speed; 4096 for precision
includeChirality	False	True for stereo-sensitive applications
Lipinski max_violations	1	0 for strict; exempt biologics entirely
mmpdb --min-pairs	3	Lower for sparse datasets
mmpdb --max-variable-size	10	Increase for macrocycle transformations
PAINS catalogs	A+B+C (all three)	Never use partial sets

Common Mistakes

• Wrong: AllChem.GetMorganFingerprintAsBitVect() → Right: rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048).GetFingerprint(mol) → Why: AllChem FP functions deprecated since RDKit 2024.03, will be removed

• Wrong: Default MolFromSmiles(smi) without error handling → Right: MolFromSmiles(smi, sanitize=False) + SanitizeMol() in try/except → Why: Default silently returns None; explicit sanitize catches partial failures

• Wrong: Lipinski as hard binary filter → Right: Allow ≤1 violation → Why: Many approved drugs violate one rule; binary filtering rejects valid candidates

• Wrong: PAINS on clinical candidates → Right: PAINS only for HTS library triage → Why: Designed for screening artifacts; flags valid advanced compounds

• Wrong: Morgan radius=1 → Right: radius=2 minimum (ECFP4) → Why: Radius 1 loses structural information; too many false-positive similarities

• Wrong: Comparing FPs with different radius or nBits → Right: Identical parameters for both FPs → Why: Tanimoto only meaningful in same parameter space

• Wrong: Ignoring stereochemistry for chiral compounds → Right: includeChirality=True in generator → Why: Default treats enantiomers as identical

• Wrong: Fixed 0.7 threshold for all FP types → Right: Validate threshold per fingerprint type and chemical series → Why: 0.7 convention is ECFP4-specific; other types need different cutoffs

• Wrong: Raw SMILES into mmpdb → Right: Pre-canonicalize with MolToSmiles(MolFromSmiles(smi)) → Why: mmpdb expects canonical SMILES; non-canonical causes fragmentation errors

• Wrong: Confusing LogP with LogD → Right: Use LogD at pH 7.4 for ionizable compounds; RDKit computes LogP only → Why: LogP overestimates lipophilicity for ionizable molecules at physiological pH

Response Format

• Lead with the command or code the user needs — explain after
• Structure as: confirm inputs → working code → key parameters explained → gotchas
• One complete working example per task; do not show every alternative
• Keep code comments minimal and functional
• Target: 50-100 lines of code with brief surrounding explanation