Zoi Tsangalidou, Ruijie (Kelly) Zhang, Sukhdeep Singh and Michael V. LeVine
Agentic cheminformatics with Google Antigravity
From the official release
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.
This is a short excerpt. Read the full announcement on the official source.
Continue on deepmind.google → (opens in a new window)Related stories

Declare and optimize with UNIQUE constraints on Databricks
Databricks Blog
Data teams often maintain uniqueness guarantees in their pipelines, but without a...

Fuel Every Agent and App With Data That Keeps Pace
Snowflake Blog
Most companies today are working hard to decide which AI agents and applications to build. But whether their data strategy will keep up, that's another matter entirely. An agent that provides answers based on outdated numbers or an app that can't see an order a customer placed fi

Cortex Training: Serverless Fine-Tuning for Open-Weight LLMs
Snowflake Blog
Successful model customization starts with high-quality training data. Snowflake’s data platform enables teams to clean, filter, enrich and label enterprise data — both structured and unstructured — into training-ready data sets with Cortex AI Functions, SQL and Snowpark. Cortex

Real-Time Analytics on Snowflake: Stream, Transform, Serve
Snowflake Blog
Every customer interaction is a chance to either earn trust or lose attention. To stay competitive, businesses need to act before that interaction ends. A financial services team needs the latest market data to discuss portfolio adjustments with their clients. A network operation

Keep Distributed Training Running with Automated Workload Recovery
NVIDIA Developer Blog
As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery... As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training prog

Diagnose Distributed AI Workload Failures with NVIDIA Attribution
NVIDIA Developer Blog
In distributed AI workloads, a single error can produce a cascade of symptoms. A GPU or fabric event may surface as NCCL errors on other ranks, scheduler state... In distributed AI workloads, a single error can produce a cascade of symptoms. A GPU or fabric event may surface as N
