Agentic cheminformatics with Google Antigravity

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.

The skills also 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 speed at which researchers can make meaningful scientific decisions is accelerating. Not long ago, the workflow demonstrated here would have taken multiple days and required significant computational expertise. Using Antigravity as a science workbench allows both computational and experimental chemists to accelerate their workflows and focus on the questions that really matter.

The cheminformatics skills are available on Google Antigravity today. Download the Antigravity App and install the Science plugin to start using these skills. Explore Science use cases here .

After downloading and curating all the data, the agent is able to perform more advanced analysis to support the work of a computational chemist. As a next step, we will perform matched molecular pair analysis, a technique used in medicinal chemistry to isolate the effects of small, discrete changes to a molecule, by comparing pairs of molecules that only differ by that change. The agent had also independently suggested this analysis, as we expect the dataset to include many informative pairs. We will also ask the agent to build a predictive model using its molecular machine learning capabilities.

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.