Agentic cheminformatics with Google Antigravity

November 3, 2026 Science

Zoi Tsangalidou, Ruijie (Kelly) Zhang, Sukhdeep Singh and Michael V. LeVine

Designing new medicines requires curating complex data from charts, figures and literature, training predictive models, and proposing candidate molecules to synthesize — a workflow that typically takes multiple days for a single iteration.

Here we show how Google Antigravity, equipped with our 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.

Chemistry is one of science’s richest public records — spanning millions of structures across journal articles and vendor catalogs. It provides a common representation that links physics and mathematics to the life and environmental sciences. Molecules and their transformations are ubiquitous and shape the everyday world.

Despite decades of progress in cheminformatics, chemists still spend too much time wrangling data, a process complicated by the fact that molecules can be represented in many ways: from text descriptions to images or data structures. The same molecule can appear as an IUPAC name in a paper, a Markush structure in a patent, or a structure-data file (SDF) in a catalog. Advanced cheminformatics systems must be able to unify these sources and deal with the many ways errors can arise in them. This task has no margin for error – alter a single atom or bond and you have described a different compound altogether, rendering a promising inhibitor or catalyst inert.

To let scientists focus on hypotheses rather than data wrangling, we built a suite of Cheminformatics Skills for Google Antigravity, turning the surface into a collaborative science workbench with full access to local tooling for both computational chemists and experimental bench chemists.