cheminformatics
Skill62 starsChanged 5 months agoMIT-0View on GitHub
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".
3 other projects use a version of this.
Adopted by
Most recently taken up by
- BuildHarbor/sciagent-skillstheir own version, first seen Nov 1, 2026
- Pratikrishi97/sciagent-skillstheir own version, first seen Aug 29, 2026
- ffsshhttiikk/opencode-agents-skillstheir own version, first seen Feb 28, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- ffsshhttiikk/opencode-agents-skills★ 2same name
- BuildHarbor/sciagent-skills★ 1same name
- Pratikrishi97/sciagent-skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository41 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 28
Fingerprint API: Use rdFingerprintGenerator module exclusively (since RDKit 2024.03).
- Very specific: line 30
SMILES validation: Always parse with sanitize=False then explicit SanitizeMol() in try/except.
- Very specific: line 81
Wrong: Default MolFromSmiles(smi) without error handling → Right: MolFromSmiles(smi, sanitize=False) + SanitizeMol() in try/except → Why: …
- Distinctive: line 104
Keep code comments minimal and functional
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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=Truein 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
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