bio-molecular-standardization
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
npx skills add BioTender-max/awesome-bio-agent-skills --skill molecular-standardization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: RDKit 2024.09+, chembl_structure_pipeline 1.2+, MolVS 0.1.1 (legacy reference only -- rdMolStandardize is current). 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 Standardization Convert raw molecular structures into a single canonical form for ML training data, deduplication, registry, and cross-database joining. Standardization is the single most underrated upstream step: skipping it causes silent ML data leakage (training and test compounds with different tautomers count as separate), bogus QSAR predictions, and database join misses. The ChEMBL pipeline (Bento 2020) and canSARchem (Ravi 2022) are the two industry references; canSARchem extends ChEMBL with canonical-tautomer-before-parent extraction. RDKit's `rdMolStandardize` implements ChEMBL-equivalent logic in C++ (the older `MolVS` Python implementation was deprecated Q1 2024). For format-
- Version Compatibility
- Standardization Pipeline Stages
- Pipeline Reconciliation
- ChEMBL Structure Pipeline (Reference Implementation)
- Full Standardization with rdMolStandardize
- Salt Stripping Edge Cases
- Tautomer Canonicalization (debated)
- Stereochemistry Standardization
- Standardization for ML Training (avoiding data leakage)
- Per-Tool Failure Modes
- ChEMBL pipeline -- inorganic salt fails
- Uncharger -- removes critical charge
- Tautomer enumerator -- combinatorial explosion
- MolVS deprecated -- ImportError on Python 3.12+
What does the bio-molecular-standardization skill do?
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill molecular-standardization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.