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-generative-designgit 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-generative-design)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chemoinformatics-generative-design"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-generative-design/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-generative-design"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chemoinformatics-generative-design.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 | $0.00119 | $0.04215 |
| Opus 5 | $0.00060 | $0.02107 |
| Sonnet 5 | $0.00024 | $0.00843 |
| Haiku 4.5 | $0.00012 | $0.00421 |
Grade A, and why
bio-generative-design 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 11d 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-generative-design — 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 — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: REINVENT 4.0+, RDKit 2024.09+, PyTorch 2.1+, MolMIM (NVIDIA BioNeMo), chemprop 2.0+.
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.
Generative Molecular Design
Generate novel molecules biased toward desired properties using deep generative models. REINVENT 4 (Loeffler et al. 2024, AstraZeneca) provides four generator families: Reinvent (de novo), Libinvent (scaffold decoration and library design), Linkinvent (linker design), and Mol2Mol (similarity-constrained molecular optimization). These support design tasks including R-group replacement and scaffold hopping and can be used with transfer learning, reinforcement learning, and curriculum learning. For specific niches: MolMIM (NVIDIA BioNeMo) for latent-space property optimization, DiffSMol / DiGress for diffusion-based generation, and JT-VAE for latent-space optimization. The art of generative design is in the scoring function: poorly-designed scoring rewards uninteresting molecules, while well-designed scoring captures both activity and developability.
For QSAR/scoring models that feed generative design, see chemoinformatics/qsar-modeling. For synthetic feasibility, see chemoinformatics/retrosynthesis. For library enumeration as alternative, see chemoinformatics/reaction-enumeration.
Generator Mode Taxonomy
| Mode | Input | Output | Use case | Fails when |
|---|---|---|---|---|
| De novo | Empty seed or training set | Novel molecules | Wide chemical space exploration | Synthetic feasibility weak |
| Scaffold decoration | Scaffold + attachment points | Decorated molecules | Series expansion | Generation diversity limited by scaffold |
| Linker design | 2 fragments | Linker molecules | PROTAC, ternary complex | Few linker geometric options |
| R-group replacement | Scaffold + existing R-groups | New R-group set | Optimize one position | Single-position only |
| Molecular optimization | Lead molecule | Improved analogs | Lead optimization | Improvement window narrow |
| Constrained generation | Hard constraints (MW, fragments) | Compliant molecules | Patent / IP design | Constraints overly restrictive |
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.
- 11d ago First seen · 329 lines · 119 tokens per session scan A b0fc2e9b4dcf
bio-generative-design is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (403 stars, last pushed today), licensed MIT. It adds 119 tokens to every session and 4,215 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-generative-design, differing in 12 lines, and is treated as a copy.
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