AI-Guided Compound Design
We use generative chemistry and machine learning to design novel small-molecule STING inhibitor candidates based on internal structure–activity relationships.
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We use generative chemistry and machine learning to design novel small-molecule STING inhibitor candidates based on internal structure–activity relationships.
Learn more →Our platform prioritizes the properties required for CNS therapeutics, including blood–brain barrier penetration, metabolic stability, solubility, safety margins, and synthetic tractability.
Learn more →Binding, activity, ADMET, and CNS exposure data are incorporated into each design cycle, enabling continuous improvement in compound ranking and lead optimization.
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Zermatt is advancing a CNS-focused STING inhibitor pipeline beginning with rare interferon-driven neuroinflammatory diseases and expanding toward broader CNS indications with shared innate immune biology.
