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Agent skill

Disease Reversal Prediction

by InternScience in InternScience/scp

Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery.

MITAuto-check passedAI & LLM Engineering

Install Disease Reversal Prediction

skills CLI
$ npx skills add InternScience/scp --skill disease-reversal-prediction -a claude-code

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

GitHub CLI
$ gh skill install InternScience/scp disease-reversal-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/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/disease-reversal-prediction .claude/skills/disease-reversal-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/

Facts

Skill name
disease-reversal-prediction
GitHub stars
170
Used in
1 other repo
Token cost
~931 tokens
SKILL.md length
216 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery.

  • Works in 2 steps: Validate SMILES - Check if input SMILES… → Calculate DLEPS Score - Predict disease…
  • Tasks that involve Embeddings
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Drug discovery and cheminformatics

What it does

Disease Reversal Prediction is an agent skill from InternScience/scp. Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery.

Its SKILL.md is about 930 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 AI & LLM Engineering, covering Embeddings and Drug discovery and cheminformatics. The licence is MIT.

When your agent uses it

  • Tasks that involve Embeddings
  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/disease-reversal-prediction”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Validate SMILES - Check if input SMILES strings are chemically valid
  2. Calculate DLEPS Score - Predict disease state reversal scores for valid molecules

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).

    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

Disease Reversal Prediction loads about 931 tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 216 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~931

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). 216 words, ~931 tokens.

Download SKILL.mdSave it as .claude/skills/disease-reversal-prediction/SKILL.md (or your agent's skills folder).
name
disease-reversal-prediction
description
Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery.
license
MIT license
metadata.skill-author
PJLab

Disease State Reversal Prediction

Usage

  1. MCP Server Definition

Use the same DrugSDAClient class as defined in the drug-screening-docking skill.

2. Disease State Reversal Prediction Workflow

This workflow validates SMILES strings and predicts their ability to reverse disease states, useful for drug repositioning and therapeutic discovery.

Workflow Steps:

  1. Validate SMILES - Check if input SMILES strings are chemically valid
  2. Calculate DLEPS Score - Predict disease state reversal scores for valid molecules

Implementation:

python
tool_client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
model_client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model")

if not await tool_client.connect() or not await model_client.connect():
    print("connection failed")
    return

## Input: List of candidate SMILES strings
smiles_list = [
    'Nc1nnc(S(=O)(=O)NCCc2ccc(O)cc2)s1',
    'COc1ccc2c(=O)cc(C(=O)N3CCN(c4ccc(F)cc4)CC3)oc2c1',
    'ABCCOOO'  # Invalid SMILES for demonstration
]

## Step 1: Validate SMILES strings
result = await tool_client.session.call_tool(
    "is_valid_smiles",
    arguments={"smiles_list": smiles_list}
)
result_data = tool_client.parse_result(result)
valid_smiles_list = [x['smiles'] for x in result_data['valid_res'] if x['is_valid'] is True]

print(f"Valid SMILES: {len(valid_smiles_list)}/{len(smiles_list)}")

## Step 2: Calculate DLEPS scores for disease state reversal
disease_name = "Aging"  # Can be: Aging, Alzheimer's, Parkinson's, etc.

result = await model_client.session.call_tool(
    "calculate_dleps_score",
    arguments={
        "smiles_list": valid_smiles_list,
        "disease_name": disease_name
    }
)
result_data = model_client.parse_result(result)

## Display results sorted by score
pred_scores = sorted(result_data['pred_scores'], key=lambda x: x['cs_score'], reverse=True)
for item in pred_scores:
    print(f"SMILES: {item['smiles']}")
    print(f"Disease Reversal Score: {item['cs_score']:.4f}\n")

await tool_client.disconnect()
await model_client.disconnect()
Tool Descriptions

DrugSDA-Tool Server:

  • is_valid_smiles: Validate SMILES strings for chemical correctness
    • Args: smiles_list (List[str])
    • Returns: valid_res with is_valid boolean for each SMILES

DrugSDA-Model Server:

  • calculate_dleps_score: Predict disease state reversal scores
    • Args: smiles_list (List[str]), disease_name (str)
    • Returns: pred_scores with cs_score (float, 0-1) for each molecule
Input/Output

Input:

  • smiles_list: List of SMILES strings to evaluate
  • disease_name: Target disease (e.g., "Aging", "Alzheimer's", "Parkinson's")

Output:

  • pred_scores: List of dictionaries containing:
    • smiles: Input SMILES string
    • cs_score: Disease reversal score (0-1, higher is better)
Score Interpretation
  • cs_score > 0.5: Strong potential for disease state reversal
  • cs_score 0.2-0.5: Moderate potential
  • cs_score < 0.2: Low potential

Molecules with higher scores are more likely to reverse the disease-associated transcriptional signature.

Supported Diseases

The model supports various diseases including but not limited to:

  • Aging
  • Alzheimer's Disease
  • Parkinson's Disease
  • Cardiovascular diseases
  • Cancer subtypes
  • Inflammatory diseases

Consult the MCP server documentation for the complete list of supported diseases.

© 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/disease-reversal-prediction 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

Disease Reversal 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.

Disease Reversal Prediction compared with similar skills
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Disease Reversal Prediction this skillInternScience/scp1701 repos~931Automated safety check: PassMIT
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Unimoljinzhezenggroup/computational-chemistry-agent-skills148—~1.5kAutomated safety check: PassLGPL-3.0-or-later
Kermt EmbedNVIDIA-BioNeMo/bionemo-agent-toolkit479—~1.7kAutomated safety check: PassApache-2.0
Kermt EmbedNVIDIA/skills3.6k1 repos~1.9kAutomated safety check: PassApache-2.0
Nvmolkit UsageNVIDIA-BioNeMo/bionemo-agent-toolkit479—~4.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Disease Reversal Prediction

What does Disease Reversal Prediction do?

Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery. Disease Reversal Prediction is an agent skill from InternScience/scp. Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery.

When should I use Disease Reversal Prediction?

Disease Reversal Prediction fits situations like: tasks that involve Embeddings; tasks that involve Drug discovery and cheminformatics.

How do I install Disease Reversal Prediction in Claude Code?

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

How do I install Disease Reversal Prediction in Codex?

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

Can I use Disease Reversal 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 InternScience/scp --skill disease-reversal-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/disease-reversal-prediction, .gemini/skills/disease-reversal-prediction, .github/skills/disease-reversal-prediction and .opencode/skills/disease-reversal-prediction in your project.

What does Disease Reversal Prediction need to run?

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

Does Disease Reversal Prediction 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 Disease Reversal 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 Disease Reversal Prediction use?

Disease Reversal Prediction 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 Disease Reversal Prediction use?

About 931 tokens (SKILL.md is roughly 3.7k 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 Disease Reversal Prediction?

Skills that share tags, products or a category with Disease Reversal Prediction: Esmfold2 (JimLiu/science-skills, 228 stars), Unimol (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars), Kermt Embed (NVIDIA-BioNeMo/bionemo-agent-toolkit, 479 stars) and Kermt Embed (NVIDIA/skills, 3.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Disease Reversal Prediction?

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.