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Agent skill

Drugsda Drug Likeness

by InternScience in InternScience/scp

Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).

MITAuto-check passedResearch & Science

Install Drugsda Drug Likeness

skills CLI
$ npx skills add InternScience/scp --skill drugsda-drug-likeness -a claude-code

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

GitHub CLI
$ gh skill install InternScience/scp drugsda-drug-likeness --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/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drugsda-drug-likeness .claude/skills/drugsda-drug-likeness && 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/

Facts

Skill name
drugsda-drug-likeness
GitHub stars
170
Used in
1 other repo
Token cost
~832 tokens
SKILL.md length
22 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).

  • Works in 2 steps: MCP Server Definition → Drug-likeness Calculation
  • Tasks that involve Drug discovery and cheminformatics
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Drugsda Drug Likeness is an agent skill from InternScience/scp. Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).

Its SKILL.md is about 830 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Drug discovery and cheminformatics. The licence is MIT.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/drugsda-drug-likeness”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. MCP Server Definition
  2. Drug-likeness Calculation

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. 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 python and tex).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Drugsda Drug Likeness loads about 832 tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 22 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~832

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 InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 22 words, ~832 tokens.

Download SKILL.mdSave it as .claude/skills/drugsda-drug-likeness/SKILL.md (or your agent's skills folder).
name
drugsda-drug-likeness
description
Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).
license
MIT license
metadata.skill-author
PJLab

Molecular Drug-likeness Metrics Calculation

Usage

1. MCP Server Definition
python
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class DrugSDAClient:    
    def __init__(self, server_url: str):
        self.server_url = server_url
        self.session = None
        
    async def connect(self):
        print(f"server url: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
            )
            self.read, self.write, self.get_session_id = await self.transport.__aenter__()
            
            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self.session_ctx.__aenter__()

            await self.session.initialize()
            session_id = self.get_session_id()
            
            print(f"✓ connect success")
            return True
            
        except Exception as e:
            print(f"✗ connect failure: {e}")
            import traceback
            traceback.print_exc()
            return False
    
    async def disconnect(self):
        try:
            if self.session:
                await self.session_ctx.__aexit__(None, None, None)
            if hasattr(self, 'transport'):
                await self.transport.__aexit__(None, None, None)
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")
    
    def parse_result(self, result):
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}
2. Drug-likeness Calculation

The description of tool calculate_mol_drug_chemistry.

tex
Compute key drug-likeness metrics for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing feature keys.
        --smiles (str): A SMILES string of smiles_list
        --qed (float): Quantitative Estimate of Drug-likeness (QED) score
        --lipinski_rule_of_5_violations (int): Number of violations of Lipinski's Rule of Five

How to use tool calculate_mol_drug_chemistry :

python
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
    print("connection failed")
    return

response = await client.session.call_tool(
    "calculate_mol_drug_chemistry",
    arguments={
        "smiles_list": smiles_list
    }
)
result = client.parse_result(response)
druglikeness_metrics = result["metrics"]

await client.disconnect()

© InternScience, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/drugsda-drug-likeness of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Drugsda Drug Likeness 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.

Drugsda Drug Likeness compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Drugsda Drug Likeness this skillInternScience/scp1701 repos~832Automated safety check: PassMIT
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Drug DiscoveryTommy-yw/RunbookHermes5461 repos~2.3kAutomated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biomedical Analysis Dispatchxjtulyc/MedgeClaw6171 repos~2kAutomated safety check: PassNone
Biopipelineslocbp-uzh/biopipelines109—~2.4kAutomated safety check: PassMIT

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Categories

Questions about Drugsda Drug Likeness

What does Drugsda Drug Likeness do?

Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format). Drugsda Drug Likeness is an agent skill from InternScience/scp. Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).

When should I use Drugsda Drug Likeness?

Drugsda Drug Likeness fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Drugsda Drug Likeness in Claude Code?

Run `npx skills add InternScience/scp --skill drugsda-drug-likeness -a claude-code`. Or copy the skill folder (skills/drugsda-drug-likeness in InternScience/scp) into .claude/skills/drugsda-drug-likeness in your project. Claude Code loads it when a task matches its description.

How do I install Drugsda Drug Likeness in Codex?

Run `npx skills add InternScience/scp --skill drugsda-drug-likeness -a codex`. Or copy the skill folder (skills/drugsda-drug-likeness in InternScience/scp) into .agents/skills/drugsda-drug-likeness in your project. Codex loads it when a task matches its description.

Can I use Drugsda Drug Likeness 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 InternScience/scp --skill drugsda-drug-likeness -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drugsda-drug-likeness, .gemini/skills/drugsda-drug-likeness, .github/skills/drugsda-drug-likeness and .opencode/skills/drugsda-drug-likeness in your project.

What does Drugsda Drug Likeness need to run?

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

Does Drugsda Drug Likeness access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Drugsda Drug Likeness 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 Drugsda Drug Likeness use?

Drugsda Drug Likeness is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drugsda Drug Likeness use?

About 832 tokens (SKILL.md is roughly 3.3k 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 Drugsda Drug Likeness?

Skills that share tags, products or a category with Drugsda Drug Likeness: Molecode (AtomFlow-AI/MoleCode, 306 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drugsda Drug Likeness?

InternScience (a GitHub organization) maintains it in InternScience/scp, which has 170 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.

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