Google DeepMind announced on November 3, 2026, that Google Antigravity, equipped with new Cheminformatics Skills, accelerates drug discovery workflows. Using real-world lung cancer treatments, an agent searches through thousands of compounds, performs matched molecular pair analysis, and proposes novel rigidified macrocycles with synthetic routes in just an hour — a workflow that typically takes multiple days for a single iteration. The authors are Zoi Tsangalidou, Ruijie (Kelly) Zhang, Sukhdeep Singh and Michael V. LeVine. The skills create a division of labour where the underlying language model handles workflow orchestration, database navigation and scientific reasoning, while deterministic chemistry tools execute molecular calculations. Grounded in RDKit, the foundational open source cheminformatics toolkit developed over two decades by a global community of contributors, alongside connectors to curated resources from ChEMBL and PubChem, the skills encode domain best practices directly into the agent's workflow. The article notes that chemists still spend too much time wrangling data, complicated by molecules being representable in many ways — from text descriptions to images or data structures — and that altering a single atom or bond describes a different compound altogether. The walkthrough highlights rigidification, a classic medicinal chemistry technique to improve drug potency, where rotatable bonds let molecules twist into many 3D shapes, making them "floppy" in solution.