bio-similarity-searching skillA
bio-similarity-searching is agent-read markdown (skill) from pku-yuangroup/openai4s: 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, MHFP6 LSH forest, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
## Version Compatibility Reference examples tested with: RDKit 2024.09+, scikit-learn 1.4+, 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 …
Read the whole file at its exact version.
How to install
mdr add pku-yuangroup/openai4s/bio-similarity-searching@git:20260821.2d1b678mdr add pku-yuangroup/openai4s/bio-similarity-searching@sha256:17b4cf8748afd9faPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_id3eqi4ndhfp5ca2)
1 badge views in 30 days
Versions
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (18563 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_id3eqi4ndhfp5ca2 GET https://markdownregistry.com/api/v1/resolve?ref=pku-yuangroup/openai4s/bio-similarity-searching GET https://markdownregistry.com/api/v1/blob/17b4cf8748afd9fa1e17c28c4d454e28fc1076dca0074c3ae054584638e80d20
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.