Recursion PharmaceuticalsScale-up

Operates one of the largest biological image and phenomics datasets and uses deep learning pipelines end-to-end for target and compound discovery; structurally positioned at exactly this intersection.

Runs AI against massively automated biological assays; as assays miniaturise toward lab-on-chip formats, their ML infrastructure maps directly onto microfluidic control.

Operates one of the largest biological imaging datasets and applies deep learning to cellular biophysical phenotypes at industrial throughput.

Business model is explicitly AI over high-content cell imaging and transcriptomics; maps cellular phenotypes at massive scale and trains deep learning models to decode them.

Built a CNN-at-scale phenomics platform for molecular biology; the same image-featurization logic is being extended toward nanoscale drug-delivery particle characterization.

Combines high-throughput biological imaging (nanoscale phenotyping) with large-scale ML; algorithmic analysis of cellular nanoscale responses to drug-nanoparticle candidates is core to their platform.

Operates one of the largest automated biological experimentation pipelines in industry; task orchestration at scale across wet-lab robots and computational biology is a core operational problem they are actively solving.