Agent skill

Drug Admet Prediction

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.

Install Drug Admet Prediction

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-admet-prediction --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drug-admet-prediction .claude/skills/drug-admet-prediction && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "drug-admet-prediction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction into .claude/skills/drug-admet-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-admet-prediction", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-admet-prediction --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/drug-admet-prediction .agents/skills/drug-admet-prediction && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "drug-admet-prediction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction into .agents/skills/drug-admet-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-admet-prediction", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-admet-prediction --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/drug-admet-prediction .cursor/skills/drug-admet-prediction && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "drug-admet-prediction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction into .cursor/skills/drug-admet-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-admet-prediction", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/drug-admet-prediction

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-admet-prediction --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/drug-admet-prediction .gemini/skills/drug-admet-prediction && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "drug-admet-prediction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction into .gemini/skills/drug-admet-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-admet-prediction", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-admet-prediction

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/drug-admet-prediction .github/skills/drug-admet-prediction && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "drug-admet-prediction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction into .github/skills/drug-admet-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-admet-prediction", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-admet-prediction --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/drug-admet-prediction .opencode/skills/drug-admet-prediction && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "drug-admet-prediction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-admet-prediction into .opencode/skills/drug-admet-prediction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-admet-prediction", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: drug-admet-prediction
GitHub stars: 176
Token cost: ~1.1k tokens
SKILL.md length: 392 words
Files: 4
Skills in repo: 129
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.

Tasks that involve Drug discovery and cheminformatics
SKILL.md covers Goal, Instructions, Examples and Constraints
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Drug Admet Prediction is an agent skill from learningmatter-mit/AtomisticSkills. Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/README.md` and `examples/compounds_admet.json`).

It sits in Research & Science, covering Drug discovery and cheminformatics. It works with RDKit and Model Context Protocol. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

Tasks that involve Drug discovery and cheminformatics

Example prompts

“/drug-admet-prediction”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drug Admet Prediction loads about 1.1k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 392 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~34
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 392 words, ~1,088 tokens.

Download SKILL.mdSave it as .claude/skills/drug-admet-prediction/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: drug-admet-prediction
description: Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.
metadata.category: drug-discovery
metadata.venv: cpu

admet-prediction

<!-- mcp-tools-note -->

[!NOTE] Steps written server.tool are MCP tool calls: drugdisc.compute_molecular_descriptors is the compute_molecular_descriptors tool of the drugdisc server (mcp__drugdisc__compute_molecular_descriptors, or mcp__plugin_atomistic-skills_drugdisc__compute_molecular_descriptors when installed as a plugin). Without a connected server, run the same tools from the shell. Tools named in one command share a process, so a model loaded by load_model stays loaded:
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python -m src.mcp_server.cli drugdisc compute_molecular_descriptors key=value

Goal

Compute ADMET-relevant physicochemical descriptors and rule-based drug-likeness heuristics from SMILES strings using RDKit.

This skill reports:

Core descriptors: molecular weight (average and exact), Wildman-Crippen cLogP, TPSA, HBD/HBA, rotatable bonds, ring counts, aromatic rings, heavy atoms, fractionCSP3, molar refractivity.
Heuristics:
- Lipinski Rule of Five (Ro5) compliance (≤ 1 violation) as a permeability/absorption triage heuristic.
- Veber oral bioavailability heuristic (RB ≤ 10 and TPSA ≤ 140 Å²; plus reporting the alternative HBD+HBA ≤ 12 condition).
- QED (Quantitative Estimate of Drug-likeness) score.

Note: This does not predict experimental ADMET endpoints (e.g., clearance, CYP inhibition, hERG, Ames, etc.). It is an early-stage physchem/heuristics screen.

Instructions

The drugdisc MCP server provides a compute_molecular_descriptors tool that can be called directly:

Single molecule analysis:

bash

drugdisc.compute_molecular_descriptors(
    smiles="CC(=O)Oc1ccccc1C(=O)O",
    output_file="aspirin_admet.json"
)

Batch analysis from a SMILES file:

bash

drugdisc.compute_molecular_descriptors(
    smiles_file="${CLAUDE_SKILL_DIR}/examples/compounds.smi",
    output_file="batch_admet.json"
)

With S/P-inclusive TPSA:

bash

drugdisc.compute_molecular_descriptors(
    smiles="OC(=O)P(=O)(O)O",
    include_sandp_tpsa=True,
    output_file="foscarnet_admet.json"
)

Examples

Example compounds.smi:

text

CN1C=NC2=C1C(=O)N(C(=O)N2C)C	caffeine
CC(=O)Oc1ccccc1C(=O)O	aspirin
CC(C)Cc1ccc(cc1)C(C)C(=O)O	ibuprofen

Run:

bash

drugdisc.compute_molecular_descriptors(
    smiles_file="${CLAUDE_SKILL_DIR}/examples/compounds.smi",
    output_file="drug_admet.json"
)

Show full SKILL.md (203 more words)Show less

Constraints

MCP Server: Requires drugdisc MCP server
Dependencies: RDKit (Chem, Descriptors, Lipinski, Crippen, QED)
Scope: Outputs physchem descriptors + rule-based heuristics only; not ML/experimental ADMET prediction
Ro5 interpretation: A "pass" is defined here as ≤ 1 violation (common industry convention)
Veber interpretation: Primary check uses TPSA ≤ 140 Å² and rotatable bonds ≤ 10, and additionally reports the alternative (HBD + HBA ≤ 12) criterion
Standardization: If SMILES contains multiple fragments (e.g., salts, "."), results are reported but flagged with a warning; consider desalting/neutralization upstream for library triage
TPSA option: By default, TPSA uses RDKit's default behavior (no S/P); include_sandp_tpsa=True includes S/P contributions
Two HBA definitions, both reported: hba is rdMolDescriptors.CalcNumHBA, the strict SMARTS acceptor count that excludes amide and pyrrole-type N with delocalised lone pairs (caffeine = 3: two carbonyl O plus one imidazole =N-). hba_lipinski is rdMolDescriptors.CalcNumLipinskiHBA, the raw N+O count Lipinski 1997 specified (caffeine = 6). Ro5 is scored on hba_lipinski, per the original paper. Do not call the Lipinski.NumHAcceptors alias: its meaning changed between rdkit 2025.09.4 and 2025.09.6 (caffeine 6 -> 3), so results computed through it are not comparable across environments. hba inherits that library change and will read 6 on rdkit <= 2025.09.4 and 3 on >= 2025.09.6; hba_lipinski is stable on both.

Author: Matthew Cox Contact: GitHub @mcox3406

Files

SKILL.md and 3 other files in skills/drug-admet-prediction of learningmatter-mit/AtomisticSkills.

SKILL.md
examples/README.md
examples/compounds.smi
examples/compounds_admet.json

Open the folder on GitHubat commit 6257444

Compare with similar skills

Drug Admet Prediction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drug Admet Prediction compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Drug Admet Prediction this skilllearningmatter-mit/AtomisticSkills	176	—	~1.1k	Automated safety check: Pass	MIT	2 days ago
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills	48k	1 repos	~3k	Automated safety check: Notes	MIT	4 days ago
Edu Chem Reactionwy51ai/edulab	1.4k	—	~1.2k	Automated safety check: Pass	Apache-2.0	11 days ago
Biopipelineslocbp-uzh/biopipelines	109	—	~2.4k	Automated safety check: Pass	MIT	9 days ago
Hcls Build Agentaws-samples/amazon-bedrock-agents-healthcare-lifesciences	274	—	~885	Automated safety check: Pass	MIT-0	8 days ago
RDKit Cheminformatics Practicesaiming-lab/AutoResearchClaw	15k	—	~708	Automated safety check: Pass	MIT	1 mo ago

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Questions about Drug Admet Prediction

What does Drug Admet Prediction do?

Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES. Drug Admet Prediction is an agent skill from learningmatter-mit/AtomisticSkills. Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.

When should I use Drug Admet Prediction?

Drug Admet Prediction fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Drug Admet Prediction in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a claude-code`. Or copy the skill folder (skills/drug-admet-prediction in learningmatter-mit/AtomisticSkills) into .claude/skills/drug-admet-prediction in your project. Claude Code loads it when a task matches its description.

How do I install Drug Admet Prediction in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a codex`. Or copy the skill folder (skills/drug-admet-prediction in learningmatter-mit/AtomisticSkills) into .agents/skills/drug-admet-prediction in your project. Codex loads it when a task matches its description.

Can I use Drug Admet Prediction in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-admet-prediction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-admet-prediction, .gemini/skills/drug-admet-prediction, .github/skills/drug-admet-prediction and .opencode/skills/drug-admet-prediction in your project.

What does Drug Admet Prediction need to run?

SKILL.md names no scripts, command-line tools or credentials: Drug Admet Prediction is instructions for the agent only. Our summary lists: Python 3.

Does Drug Admet Prediction access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Drug Admet Prediction safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Drug Admet Prediction use?

Drug Admet Prediction is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug Admet Prediction use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Drug Admet Prediction?

Skills that share tags, products or a category with Drug Admet Prediction: DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), Edu Chem Reaction (wy51ai/edulab, 1.4k stars), Biopipelines (locbp-uzh/biopipelines, 109 stars) and Hcls Build Agent (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Admet Prediction?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Drug Admet Prediction

Install Drug Admet Prediction

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

admet-prediction

Goal

Instructions

Examples

Constraints

Files

Compare with similar skills

Similar skills

DiffDock Molecular Docking

Edu Chem Reaction

Biopipelines

Hcls Build Agent

RDKit Cheminformatics Practices

Rowan

More from learningmatter-mit/AtomisticSkills

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Questions about Drug Admet Prediction

Install Drug Admet Prediction

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

admet-prediction

Goal

Instructions

Examples

Constraints

Files

Compare with similar skills

Similar skills

More from learningmatter-mit/AtomisticSkills

Related

Questions about Drug Admet Prediction