VivoSim Labs, Inc (VIVS) Fair Value & Analysis
Healthcare · US · Market cap $3.2M
Fair value as of: Jun 25, 2026
Analysis
VivoSim Labs, Inc (VIVS) currently trades at $1.09, while our model-based Fair Value estimate is $0.9834 — implying the stock looks roughly 9.8% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: low).
About the company
VivoSim Labs, Inc., a pharmaceutical and biotechnology services company, provides testing of drugs and drug candidates in three-dimensional (3D) human tissue models of liver and intestine. It offers partners liver and intestinal toxicology insights through its new approach methodologies (NAM) models; and bespoke services in the areas of investigational toxicology, mechanism of drug action elucidation, and other applications of complex human tissue models. The company also provides liver toxicology predictive screening and research services, as well as works on predicting and studying the intestinal side effect profiles of drugs that are therapeutic candidates of pharmaceutical and biotech companies at various stages of drug development; and uses its proprietary technologies to build functional 3D human tissues that mimic key aspects of native human tissue composition, architecture, function, and disease. In addition, it offers 3D human tissue platform that develops novel human norma…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.