deepmind.googlearticleAccelerating the search for antimicrobial peptides with Google Antigravity
Google DeepMind describes using Google Antigravity as an agentic science workbench to find antimicrobial peptide candidates in Streptococcus pseudopneumoniae. The agent orchestrates queries across UniProt and other databases, uses ProtNLM function predictions for uncharacterized proteins, and pulls structures from the AlphaFold Database. A new Biomolecular Structure Viewer shares 3D state with the agent in both directions, so residues the researcher highlights or pins become context for the agent's reasoning. The resulting lead has predicted activity against respiratory-tract pathogens and is going into wet-lab validation.
TL;DR DeepMind shows a human-in-the-loop Antigravity agent session, with a shared 3D structure viewer and ProtNLM, that finds a candidate antimicrobial peptide from UniProt.
Organisations named here
- Google DeepMind
- UniProt
- Darobactin
- Dynobactin
- Lariocidin
- Nisin
- Streptococcus pseudopneumoniae
Technologies named here
- Google Antigravity
- ProtNLM
- Biomolecular Structure Viewer
- UniProt
- AlphaFold database
- AlphaFold
Techniques named here
- Collaborative researcher–agent multi-turn session
- Shared visual state between agent and researcher
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