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-qsar-modelinggit 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-qsar-modeling)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-qsar-modeling"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-qsar-modeling/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-qsar-modeling"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-qsar-modeling.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 | $0.00120 | $0.05045 |
| Opus 5 | $0.00060 | $0.02523 |
| Sonnet 5 | $0.00024 | $0.01009 |
| Haiku 4.5 | $0.00012 | $0.00505 |
Grade A, and why
bio-qsar-modeling 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 8d 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
98% identical to bio-qsar-modeling — 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 — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples target: chemprop 2.2.x (major API change from 1.x), RDKit 2024.09+, scikit-learn >=1.4,<1.6, MAPIE >=0.8,<1.0 for the MapieRegressor example, shap 0.44+, and pytorch 2.1+. Recheck examples before widening these bounds because Chemprop, scikit-learn calibration, and MAPIE interfaces evolve independently.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
chemprop train --help(chemprop 2.x);chemprop_train --help(1.x legacy)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
QSAR Modeling
Build quantitative structure-activity relationship models from molecular structure inputs. The choice of model, featurization, and split strategy determines whether the model captures transferable chemical signal or memorizes the training data. chemprop D-MPNN with optional Morgan / RDKit descriptors is a useful open-source approach; transformer-based methods (MolFormer, Uni-Mol, ChemBERTa) should be compared on the same split and endpoint. The OECD validation principles support transparent documentation and evaluation of (Q)SAR models, but following them does not by itself confer regulatory acceptance.
For descriptor/fingerprint choices, see chemoinformatics/molecular-descriptors. For ADMET-specific QSAR, see chemoinformatics/admet-prediction. For molecular standardization (critical upstream), see chemoinformatics/molecular-standardization.
Model Taxonomy
| Model | Architecture | Use case | Fails when |
|---|---|---|---|
| Random Forest + ECFP4 | Classical baseline | Small-data comparison, interpretability | May miss signal not represented by the fingerprint |
| chemprop D-MPNN | Directed message passing | Graph-learning candidate to benchmark | Can overfit when data are sparse or biased |
| chemprop D-MPNN + RDKit 2D | Hybrid graph + descriptors | Useful hybrid baseline; compare on the same split | Diminishing returns at large data |
| MolFormer | SMILES transformer | Large public training data benefit | Compute overhead; OOD risk |
| Uni-Mol | 3D-aware transformer | 3D-relevant endpoints (binding) | Requires 3D conformers |
| ChemBERTa-2 | SMILES transformer pretrained on up to 77M molecules | SMILES language-model baseline | Fine-tuning benefit is endpoint- and split-dependent |
| Gaussian Process + ECFP4 | Probabilistic | Active learning; uncertainty | O(N^3) scaling |
| MultiTask DNN | Joint training | Multiple endpoints | Data must overlap |
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.
- 8d ago First seen · 360 lines · 120 tokens per session scan A a0be816d139a
bio-qsar-modeling is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (403 stars, last pushed yesterday), licensed MIT. It adds 120 tokens to every session and 5,045 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-qsar-modeling, differing in 12 lines, and is treated as a copy.
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