---
name: al-660-cut-days-of-drug-discovery-data-wrangling-to-about-an-hou
description: "Google DeepMind's Nov 3, 2026 blog introduces Cheminformatics Skills for Google Antigravity. In a walkthrough on lung-cancer tyrosine kinase inhibitors (crizotinib to lorlatinib-style rigidification), an agent searches literature for thousands of compounds, filters noise such as buffers, builds an interactive viewer, runs matched molecular pair analysis, trains potency models with scaffold or chronological splits, and proposes new macrocycles with synthetic routes and predicted potency--work the authors say used to take multiple days for one iteration, done in about an hour in the demo. Skills ground calculations in RDKit with ChEMBL and PubChem connectors. The post stresses that designs are computational hypotheses that must be synthesized and validated experimentally, not finished drugs. Use when the user wants help with agentic cheminformatics for drug design (research) for a small business."
---

# Cut days of drug-discovery data wrangling to about an hour: give a chemistry AI agent Cheminformatics Skills so it curates compounds from literature, runs matched molecular pair analysis, trains potency models, and proposes synthesis routes you still validate in the lab

Google DeepMind's Nov 3, 2026 blog introduces Cheminformatics Skills for Google Antigravity. In a walkthrough on lung-cancer tyrosine kinase inhibitors (crizotinib to lorlatinib-style rigidification), an agent searches literature for thousands of compounds, filters noise such as buffers, builds an interactive viewer, runs matched molecular pair analysis, trains potency models with scaffold or chronological splits, and proposes new macrocycles with synthetic routes and predicted potency--work the authors say used to take multiple days for one iteration, done in about an hour in the demo. Skills ground calculations in RDKit with ChEMBL and PubChem connectors. The post stresses that designs are computational hypotheses that must be synthesized and validated experimentally, not finished drugs.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- E.g. rigidify a floppy inhibitor for target X
- Names or links
- RDKit, ChEMBL, PubChem, local files
- E.g. cyclization / macrocyclization

## Instructions

Do the task below with the user's inputs in place of each [bracket].

Act as a cheminformatics research partner for a medicinal-chemistry loop. Goal: [e.g. rigidify a floppy inhibitor for target X]. Starting molecules or papers: [names or links]. Tools I can use: [RDKit, ChEMBL, PubChem, local files]. 1) Search and curate related compounds; drop obvious non-actives like solvents or buffers; cite every source. 2) Summarize the set: count, potency ranges if present, and any clear trend (for example fewer rotatable bonds vs higher potency). 3) Run or outline a matched molecular pair analysis for the change I care about ([e.g. cyclization / macrocyclization]) and say what the pairs actually show--no invented numbers. 4) Propose how to train a simple potency model (features, split type, how I'll check it) and what would make the model untrustworthy. 5) Suggest up to 5 new designs with a one-line chemistry rationale and a plausible synthesis sketch from documented reaction types. Label every design as a hypothesis for lab validation. Never claim a compound is a drug; never invent assay data I didn't provide.

## Safety checks (share these with the user)

- Read and edit the draft before it reaches a customer or goes public. If you later automate this prompt, start with "draft and notify me", not "send automatically".
- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: Google Antigravity with Cheminformatics Skills, RDKit, ChEMBL, PubChem, Claude or Gemini
- Based on this real-world example: https://deepmind.google/blog/agentic-cheminformatics-with-google-antigravity/
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-660-cut-days-of-drug-discovery-data-wrangling-to-about-an-hour-g/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/