Google Antigravity agents speed up antimicrobial peptide discovery; lead enters wet-lab tests
In an October 15 technical blog, Google DeepMind described how researchers used Google Antigravity, its agentic development platform, to accelerate antimicrobial peptide (AMP) discovery: the scientist sets the biological goal and steers strategy while the agent handles cross-database orchestration, structural analysis and literature synthesis, compressing days of fragmented database triage into a single session. The platform uses ProtNLM, a deep learning model that predicts biological function directly from amino acid sequences, to enrich millions of uncharacterized UniProt entries with high-confidence functional labels, and adds an embedded Biomolecular Structure Viewer that gives researcher and agent a shared 3D canvas for annotation and structure-based questions. The post says the team uncovered a novel AMP candidate in Streptococcus pseudopneumoniae with predicted activity against pathogens involved in respiratory tract infections, and that the lead plus several others from the same pipeline are now in wet-lab validation.
It is a concrete example of an AI agent platform being used inside a real scientific workflow, but the candidate's value still depends on wet-lab results that have not yet been reported.