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 GPTomics/bioSkills --skill pharmacophore-modelinggit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/pharmacophore-modeling)<a href="https://agentmods.dev/skills/gptomics/bioskills/pharmacophore-modeling"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/pharmacophore-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/gptomics/bioskills/pharmacophore-modeling"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/pharmacophore-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 | $0.00138 | $0.04410 |
| Opus 5 | $0.00069 | $0.02205 |
| Sonnet 5 | $0.00028 | $0.00882 |
| Haiku 4.5 | $0.00014 | $0.00441 |
Grade A, and why
bio-pharmacophore-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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-pharmacophore-modeling — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 294 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+, Pharmit web service, and PLIP 2.4+ (interaction analysis). Verify the deployed Pharmit/Pharmer interface and query format before automation.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show rdkitthenhelp(rdkit.Chem.Pharm3D)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.
Pharmacophore Modeling
Build 3D pharmacophore queries that capture the essential interaction features of a ligand-target binding event. A pharmacophore is the spatial arrangement of pharmacophore features (donor, acceptor, hydrophobe, aromatic, charged) sufficient for activity, abstracted from any specific chemotype. Use pharmacophores for scaffold hopping, virtual-screening prefilters, and cross-target SAR transfer. Derive interaction features directly from a co-crystal when available, use apo2ph4 to derive models from an apo pocket (Heider et al. 2023), or align known actives for a ligand-based model. PharmacoForge generates candidate 3D pharmacophores conditioned on a protein pocket; those pharmacophores can then retrieve matching molecules from a library (Flynn et al. 2025).
For 2D scaffold-based searches, see chemoinformatics/scaffold-analysis. For 3D shape similarity, see chemoinformatics/shape-similarity. For protein-ligand interaction analysis, see chemoinformatics/virtual-screening.
Pharmacophore Feature Types
| Feature | Common shorthand | Definition | Geometric tolerance |
|---|---|---|---|
| H-bond donor | D | -OH, -NH | 1.0-1.5 Å |
| H-bond acceptor | A | sp2 O / N (lone pair) | 1.0-1.5 Å |
| Hydrophobe | H | sp3 C / aromatic ring centroid | 1.5-2.0 Å |
| Aromatic ring | R | Aromatic ring centroid + normal | 1.0-1.5 Å |
| Positive ionizable | P | -NH3+, -NR3+ | 1.0-1.5 Å |
| Negative ionizable | N | -COO-, -SO3- | 1.0-1.5 Å |
| Halogen | X | Cl, Br, I (halogen bond donor) | 1.0-1.5 Å |
| Metal coordination | M | sp/sp2 N/O near metal | 0.5-1.0 Å |
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.
- 10d ago First seen · 294 lines · 138 tokens per session scan A 7d5e2ee2b01d
bio-pharmacophore-modeling is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 138 tokens to every session and 4,410 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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