Circle Pharma vs Tempus
Side-by-side comparison of AI visibility scores, market position, and capabilities
Circle Pharma develops orally deliverable macrocycle drug candidates targeting intracellular protein-protein interactions in cancer; raised $90M Series B in 2023 led by Novo Holdings and Foresite Capital; headquartered in San Francisco;
About
Circle Pharma is a clinical-stage biopharmaceutical company founded in 2016 by Matthew Jacobson, Siegfried Bhagwati, and Grace Aldrovandi, headquartered in San Francisco, California. The company is developing a new class of therapeutics — cell-permeable macrocycles — designed to inhibit intracellular protein-protein interactions (PPIs) that have historically been considered "undruggable." Protein-protein interactions control critical cellular processes including cell division, apoptosis (programmed cell death), and gene expression. Dysregulation of these interactions drives many cancers, but their large, flat interaction surfaces are difficult to block with small molecules, and biologics (antibodies) cannot cross cell membranes to reach intracellular targets. Circle Pharma's macrocycles are medium-sized ring-shaped molecules engineered to be both cell-permeable and orally bioavailable — a combination rarely achieved — enabling them to reach and inhibit these targets.
Publicly traded AI precision medicine company (Nasdaq: TEM); $1.27B revenue in 2025 (+83% YoY); $14B+ market cap; genomic profiling, algorithmic diagnostics, and clinical trial matching across oncology, neurology, and cardiology.
About
Tempus AI is a technology company applying artificial intelligence to advance precision medicine across oncology, neurology, psychiatry, and cardiology. Founded in 2015 by Eric Lefkofsky (co-founder of Groupon) and headquartered in Chicago, Illinois, Tempus built the world's largest library of clinical and molecular data and applies AI to help physicians make more personalized, data-driven treatment decisions. The company's core offerings include genomic profiling (sequencing tumor DNA to identify mutations and actionable targets), algorithmic diagnostic tests (AI-driven companion diagnostics for cancer treatment selection), clinical trial matching (connecting eligible patients to appropriate studies), and data licensing to pharmaceutical and biotech partners for drug discovery and clinical development.
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