cheminformatics

name	cheminformatics
description	Cheminformatics pipeline for small-molecule property calculation, filtering, and similarity analysis using RDKit. Use when the user asks to compute molecular descriptors, filter compounds by Lipinski or Veber rules, detect PAINS, calculate fingerprint similarity, run matched molecular pair analysis, generate ADMET descriptors, or process SMILES. Triggers include "RDKit", "molecular descriptors", "Lipinski", "rule of five", "Veber", "PAINS", "pan-assay interference", "Morgan fingerprint", "Tanimoto", "fingerprint similarity", "matched molecular pair", "MMP", "mmpdb", "ADMET", "druglikeness", "SMILES", "cheminformatics", "compound filtering", "chemical similarity".
usage	Invoke when computing molecular properties, filtering compound libraries, calculating fingerprint similarity, or running matched molecular pair analysis with RDKit.
version	1.0.0
validated_against	{"date":"2025-01-15T00:00:00.000Z","packages":{"rdkit":"2024.03","mmpdb":"3.1"}}
tags	["skill","category:pipeline","cheminformatics","drug-discovery","rdkit","hcls"]

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