Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PKU-YuanGroup/OpenAI4S --skill bio-chemoinformatics-molecular-descriptorsgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-molecular-descriptors)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-molecular-descriptors"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-molecular-descriptors/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-molecular-descriptors"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-molecular-descriptors.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.05173 |
| Opus 5 | $0.00057 | $0.02586 |
| Sonnet 5 | $0.00023 | $0.01035 |
| Haiku 4.5 | $0.00011 | $0.00517 |
Grade A, and why
bio-molecular-descriptors scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to bio-molecular-descriptors — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: RDKit 2024.09+, numpy 1.26+, pandas 2.2+, map4 1.1+ (MAP4), mhfp 1.9+. Use mapchiral separately when the stereochemistry-aware MAP4C fingerprint is intended.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(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 Descriptors
Featurize molecules for similarity search, QSAR, virtual screening, or ML. Fingerprint performance is dataset- and objective-dependent: ECFP4 is a strong drug-like baseline, atom-pair and topological-torsion fingerprints expose longer-range topology, MAP4/MHFP6 target broader chemical-space searches, and 3D conformer-based descriptors are needed when shape and stereochemistry matter.
For canonicalization before featurization, see chemoinformatics/molecular-standardization. For 3D-only descriptors, see chemoinformatics/conformer-generation.
Fingerprint Taxonomy
| Fingerprint | Type | Radius/Path | Bits | Use case | Fails when |
|---|---|---|---|---|---|
| Morgan (ECFP) | Circular | r=2 (ECFP4), r=3 (ECFP6) | 2048 typical | Drug-like similarity, ML default | Loses long-range topology; bit collisions at low nBits |
| FCFP | Functional Morgan | r=2 default | 2048 | Pharmacophore-aware similarity | Same caveats as ECFP; less specific |
| MACCS | Substructure key | 166 fixed bits | 167 | Quick fingerprint, drug-likeness | Too sparse for large diverse libraries |
| RDKit FP | Path/subgraph-based | paths and branched subgraphs up to 7 bonds by default | 2048 | RDKit-native ECFP alternative | Drug-like only; not optimal for scaffold hopping |
| AtomPair | Pair + topological distance | All atom pairs | 2048 | Long-range topological similarity | Slower than ECFP; harder to interpret |
| TopologicalTorsion | 4-atom torsion | All TT | 2048 | Path-pattern similarity | Like AP, slower than ECFP |
| Avalon | Substructure + atom pairs | Mixed | 512/1024 | Fast similarity | Less standard; older |
| MAP4 (MinHashed atom-pair) | MinHash atom-pair | r=1,2 | 1024/2048 | Biological + metabolite diversity | map4 library required; slower hash |
| MHFP6 (MinHash) | MinHash ECFP-like | r=3 (diam 6) | 2048 | Large-library nearest-neighbor with a compatible MinHash/LSH index | Different distance semantics from folded-bit Tanimoto |
| Pharm2D | 2D pharmacophore | feature pairs/triplets | sparse | Pharmacophore search | Sparse, slower |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 267 lines · 114 tokens per session scan A 327371c49fbf
bio-molecular-descriptors is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 114 tokens to every session and 5,173 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bio-molecular-descriptors, differing in 12 lines, and is treated as a copy.
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