bio-similarity-searching
Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.
npx skills add BioTender-max/awesome-bio-agent-skills --skill similarity-searching --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+, scikit-learn 1.4+, annoy 1.17+, mhfp 1.9+. 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. # Similarity Searching Find structurally similar compounds and cluster libraries by similarity. The choice of similarity coefficient and fingerprint is **task-aware**: Tanimoto for symmetric similarity in lead optimization, Tversky for asymmetric "substructure-like" queries, Dice for higher sensitivity in low-similarity regimes, and MaxCommon Substructure (MCS) for scaffold-hopping. Tanimoto similarity above 0.7 is not a guarantee of activity preservation; activity cliffs (similar molecules with dissimilar activities) are common (Maggiora 2014). For fingerprint choice, see `chemoinformatics/molecular-descriptors`. For 3D shape similarity, see `chemoinformatics/shape-similarity`. ## Similarity Coefficient Taxonomy | Coefficient | Formula | Range | Symmetr
- Version Compatibility
- Similarity Coefficient Taxonomy
- When to Use Which Coefficient
- Tanimoto Thresholds (calibrated against medchem judgment)
- Decision Tree by Scenario
- Tanimoto Similarity (single query, large library)
- Tversky for Asymmetric Substructure-Like Search
- Butina Clustering
- Diversity Selection (MaxMin)
- Maximum Common Substructure
- Activity Cliff Diagnosis
- Large-Library Nearest Neighbor (MHFP6 + Annoy)
- Per-Tool Failure Modes
- ECFP4 Tanimoto -- saturation in diverse libraries
What does the bio-similarity-searching skill do?
Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill similarity-searching --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.