10x Genomics vs Insilico Medicine
Side-by-side comparison of AI visibility scores, market position, and capabilities
Leading single-cell genomics platform; $642M FY2025 revenue. Chromium and Visium spatial platforms power cell-type discovery in oncology, neuroscience, and immunology.
About
10x Genomics was founded in 2012 in Pleasanton, California by Serge Saxonov, Ben Hindson, and Kevin Ness. The company developed the Chromium platform for single-cell RNA sequencing (scRNA-seq), enabling researchers to analyze gene expression across thousands of individual cells simultaneously—a capability that transformed cell biology, immunology, and cancer research. The Visium Spatial Gene Expression platform extended this to map gene expression within intact tissue architecture.\n\n10x Genomics' instruments and consumables are used by leading academic research centers, pharmaceutical companies, and translational medicine teams to profile the cellular composition of tumors, map the nervous system, characterize immune responses, and develop cell therapies. The company reported full-year 2025 revenue of $642.8 million, up from $610.8 million in 2024, and guides to $600–$625 million for 2026 amid cautious biotech funding conditions.\n\nThe company is transitioning toward a broader multi-omics portfolio including ATAC-seq, protein detection, and spatial multi-omics, while defending market position against competition from Parse Biosciences, Vizgen, and others. Despite near-term funding headwinds in academic markets, 10x Genomics remains a foundational infrastructure provider for the cell atlas era of biology.
Insilico Medicine (HKEX: 3696) raised $293M, posted $56.2M revenue in 2025, and inked a $2.75B Eli Lilly deal in 2026 using generative AI to discover drug targets in cancer and CNS.
About
Insilico Medicine is a clinical-stage biotechnology company founded in 2014 that leverages generative AI for drug discovery and development. Headquartered in Boston with offices in Hong Kong and New York, the company has built AI platforms utilizing deep generative models, reinforcement learning, and transformers to discover novel drug targets and generate molecular structures with desired properties. Its pipeline spans cancer, fibrosis, CNS diseases, infectious diseases, autoimmune conditions, and aging-related diseases.
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