{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "k-dense-ai-scientific-agent-skills--screen-compounds-for-drug-likeness",
 "repo": "K-Dense-AI/scientific-agent-skills",
 "repo_url": "https://github.com/K-Dense-AI/scientific-agent-skills",
 "repo_slug": "k-dense-ai-scientific-agent-skills",
 "title": "Screen a compound list for drug-like properties",
 "page_title": "How to screen a compound list for drug-like properties with Scientific Agent Skills",
 "summary": "Use RDKit-based skills to predict molecular properties and flag ADMET concerns in a candidate set.",
 "who": "A medicinal chemist with a list of candidate molecules who needs a first-pass property filter before wet-lab work.",
 "problem": "Checking many structures one at a time for properties and ADMET flags takes time and is easy to do inconsistently.",
 "outcome": "A ranked table of candidates with computed properties and notes on ADMET concerns, with the code used to compute them.",
 "steps": [
  "`git clone https://github.com/K-Dense-AI/scientific-agent-skills` (inferred, check the README)",
  "Follow the install steps in the Getting Started section of the README for your agent host.",
  "Find the cheminformatics skill folder under skills/ that covers RDKit and molecular property prediction.",
  "`pip install rdkit` (inferred, check the README)",
  "Give your agent a file of SMILES strings and ask for property and ADMET screening."
 ],
 "agent_prompt": "Using the scientific-agent-skills repo and its RDKit cheminformatics skill, compute molecular properties and ADMET flags for the SMILES in <file of SMILES strings>. Return a ranked table with the computed values and the code used. Verify that every input structure parses and that no row is silently dropped.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Advanced",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README lists these screening topics but does not describe a full pipeline, so the agent must build and check the workflow itself.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "An agent host that supports Agent Skills, such as Claude Code, Cursor, or Codex",
  "Git, to clone the repository (inferred)",
  "Python, since the repository language is Python",
  "API keys or accounts for any registered or licensed database you plan to query"
 ],
 "risk": "sensitive",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/k-dense-ai-scientific-agent-skills/screen-compounds-for-drug-likeness/",
 "md": "https://dedupedai.com/use-cases/k-dense-ai-scientific-agent-skills/screen-compounds-for-drug-likeness/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/k-dense-ai-scientific-agent-skills--screen-compounds-for-drug-likeness.json",
 "api_version": "v1"
}