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Drug Discovery Informatics

Checked
omer-metin·vlatest·other·Apache-2.0

Analyzes vast pharmaceutical data to accelerate drug discovery by identifying promising compounds and predicting efficacy – a crucial tool for faster innovation.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add omer-metin/skills-for-antigravity --skill drug-discovery-informatics -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

omer-metin

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned omer-metin/skills-for-antigravity at e8dcf4e873.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
e8dcf4e873
Manifest
skills/drug-discovery-informatics/SKILL.md
Scanned
4 files, 1,002 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill leverages computational methods to analyze and interpret complex biological data relevant to drug discovery. It can predict the efficacy of compounds, identify potential drug targets, and optimize lead candidates based on molecular properties and interactions. The tool supports researchers in accelerating the drug development process by providing data-driven insights.

When to use it

  • Target Identification: When needing to find new biological targets for therapeutic intervention based on disease pathways or genetic data.
  • Lead Optimization: To improve the potency, selectivity, and safety profile of promising drug candidates.
  • Virtual Screening: For efficiently screening large libraries of compounds to identify potential hits against a specific target.
  • Predicting Drug Interactions: To anticipate and mitigate potential adverse effects by predicting how drugs might interact with each other or biological systems.

Key capabilities

  • Target prediction
  • Lead optimization
  • Virtual screening
  • Drug interaction prediction

Example prompts

  • "Identify potential drug targets for Alzheimer's disease based on recent genomic research."
  • "Optimize the binding affinity of compound X to target Y while minimizing off-target effects."
  • "Perform a virtual screen of 10,000 compounds against protein Z and rank them by predicted efficacy."

Tips & gotchas

The accuracy of predictions depends heavily on the quality and completeness of the underlying data. Ensure that input data is properly formatted and validated for optimal results.