Allcyte: Breaking new ground in cancer precision medicine with AI and Google Cloud

Allcyte found that conventional anticancer-drug testing methods didn't capture disease complexity well enough to predict clinical effectiveness, and processing terabytes of microscopy data took weeks; as a startup it couldn't afford up-front hardware for the GPU-based deep learning needed to scale from blood cancers to complex solid tumors. It built its pipeline on Google Cloud Storage, Compute Engine, GKE, Cloud Build, the Cloud Life Sciences API and Cloud Operations Suite. Allcyte now processes 250,000 images (about 300 GB) daily and analyzes 200 million individual cells per analysis in days instead of weeks, with a 10x reduction in long-term storage costs via lifecycle rules; CSO Gregory Vladimer said the flexibility means "we don't worry about infrastructure... we're free to engineer our primary models, too."

Google supplies AI to pharma through several distinct lines — Gemini/Vertex AI as an enterprise platform, and AlphaFold-based drug design licensed commercially through Isomorphic Labs.

This sits in the Build stage of the AI use-case lifecycle: Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.