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 agentmods add skills/kdevos12/alkyl/pharmacophorenpx skills add Kdevos12/ALKYL --skill pharmacophoregit clone --depth 1 https://github.com/Kdevos12/ALKYLWrote 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/kdevos12/alkyl/pharmacophore)<a href="https://agentmods.dev/skills/kdevos12/alkyl/pharmacophore"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/pharmacophore.svg" alt="Measured on agentmods" 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 | $0.00091 | $0.00946 |
| Opus 5 | $0.00046 | $0.00473 |
| Sonnet 5 | $0.00018 | $0.00189 |
| Haiku 4.5 | $0.00009 | $0.00095 |
Grade A, and why
pharmacophore 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pharmacophore Modeling
A pharmacophore defines the minimum set of 3D chemical features — their types, spatial arrangement, and tolerance spheres — required for biological activity. Used for scaffold hopping, virtual screening, and SAR hypothesis generation.
When to Use This Skill
- Derive pharmacophore from a co-crystal structure (structure-based)
- Extract common features from a set of active ligands (ligand-based)
- Screen a compound library for pharmacophore matches (VS)
- Compute pharmacophore fingerprints for similarity searching
- Identify scaffold-hopping opportunities (2D-diverse but 3D-similar)
- Validate binding mode hypotheses against active/inactive SAR data
Feature Types
| Feature | Code | SMARTS definition (simplified) |
|---|---|---|
| H-bond donor | HBD | [N!H0,O!H0,n!H0] |
| H-bond acceptor | HBA | [N,O,F,n,o,s] (lone pairs) |
| Aromatic | AR | any aromatic ring |
| Hydrophobic | HYD | [c,s,C,S,Br,I,Cl] non-polar |
| Positive ionizable | POS | [NH2,NH3+,guanidine,amidine] |
| Negative ionizable | NEG | [COOH,SO3H,PO4H,COO-] |
| Exclusion volume | XV | regions sterically forbidden |
Quick Start
from rdkit import Chem
from rdkit.Chem import AllChem, MolStandardize
from rdkit.Chem.Pharm3D import Pharmacophore, EmbedLib
from rdkit.Chem import rdMolChemicalFeatures
# Load feature factory (FDEF file bundled with RDKit)
import os
from rdkit import RDConfig
fdefName = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef')
factory = rdMolChemicalFeatures.BuildFeatureFactory(fdefName)
# Get features from a molecule
mol = Chem.MolFromSmiles('c1ccc(cc1)C(=O)O') # benzoic acid
feats = factory.GetFeaturesForMol(mol)
for feat in feats:
print(f"{feat.GetFamily():12s} {feat.GetType():20s} atoms={list(feat.GetAtomIds())}")
Router — What to Read
| Task | Reference |
|---|---|
| Feature types, SMARTS, exclusion volumes, RDKit FDEF format | references/features-theory.md |
| Pharm2D fingerprints (Gobbi), Pharm3D matching, feature factory | references/rdkit-pharmacophore.md |
| Derive pharmacophore from protein-ligand co-crystal / interactions | references/structure-based.md |
| Common-feature pharmacophore from a set of active ligands | references/ligand-based.md |
| Pharmacophore VS workflow: conformer gen, hit scoring, enrichment | references/vs-workflow.md |
What ships with it
5 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.
- 6d ago First seen · 78 lines · 91 tokens per session scan A 60d0a0f2b9ca
pharmacophore is a skill published in the GitHub repository Kdevos12/ALKYL (6 stars, last pushed 5mo ago), licensed MIT. It adds 91 tokens to every session and 946 once invoked, about $0.0005 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 2026-08-31.
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