Google DeepMind has published a walkthrough showing how its Google Antigravity platform, paired with a set of Cheminformatics Skills, can speed up drug discovery workflows. The post centres on agentic AI, where a system carries out a sequence of chemistry tasks, such as searching compound collections, rather than answering a single question. The headline demonstration is the proposal of novel rigidified macrocycles, ring-shaped molecules whose flexibility is constrained. Structural constraints of this kind are a design approach medicinal chemists often explore when optimising candidate molecules. The show notes present the work as a tutorial-style example of how these tools can be applied, rather than as a report on a new drug or clinical result. For Australian businesses and professionals, this changes little immediately for most organisations. Pharmaceutical, biotech and academic chemistry teams may find the workflow worth testing in a sandbox, but they should check the platform's cost and data-handling terms and assess intellectual property risk before feeding in proprietary structures. The Privacy Act is unlikely to be the main constraint for compound data unless personal information is involved, while any therapeutic work would still fall under TGA requirements.