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reproducible in-silico discovery at pharma scale
as biological foundation models gain traction, pharma research is entering a new phase. scientists can now test hypotheses computationally before stepping into the wet lab. but between model outputs and real scientific decisions, workflows remain fragmented, slow, and hard to reproduce. helical addresses this gap with a virtual ai lab designed to turn foundation models into decision-ready discovery systems.
founded by operators across tech, machine learning, and clinical research, helical brings biologists and ml engineers into one shared environment. by unifying data, models, and results, the platform enables teams to move from hypothesis to validated insight at the speed of inference, transforming in-silico discovery into a scalable, reproducible engine for pharma r&d.
building the application layer for bio foundation models
helical is creating the virtual ai lab for pharma, an application layer that translates biological foundation models into reproducible discovery workflows. the platform combines a virtual lab for biologists with a model factory for ml engineers, built on shared infrastructure that closes the gap between computational prediction and biological decision-making. already deployed across multiple top-20 pharma programs, including a collaboration with pfizer, helical is compressing discovery timelines from years to weeks across target identification, biomarker discovery, and therapeutic design.
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