PolyModels Hub Raises £7M Led By Molten Ventures And Marathon VC
Nov 20, 2025 | By Kailee Rainse

London-based PolyModels Hub has raised £7 million in Series A funding from Molten Ventures, alongside Marathon Venture Capital.
SUMMARY
- London-based PolyModels Hub has raised £7 million in Series A funding from Molten Ventures, alongside Marathon Venture Capital.
The biotech software company is building the digital backbone for biopharma process development, transforming how medicines are designed, developed, and manufactured.
Its platform, ModelFlow, integrates process knowledge, modelling, simulation, and workflow automation into a single environment, enabling scientists and engineers to design, test, and optimise drug processes faster and more efficiently. This helps biopharma teams cut time, cost, and risk while enhancing quality and compliance.
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Co-founder and CEO Antonio Benedetti notes that biopharma is undergoing a significant digital transformation. He highlights that PolyModels Hub’s technology has already delivered measurable impact for major pharmaceutical partners within just 18 months with collaborations now scaling globally.
We’re investing deeply in complex biologics and advancing our platform to empower scientists with the model-based solutions they deserve to transform molecules into medicines for the 21st century.
With the new funding, PolyModels Hub will expand its engineering and product teams, advance its core platform, and broaden its reach across global pharmaceutical companies.
The company will focus on delivering scalable solutions for process and regulatory workflows, supporting the wider digitalisation of biopharma development and helping bring new medicines to patients more efficiently.
About PolyModels Hub
PolyModels Hub, founded in 2023, is a biotech software company building the digital backbone for biopharma process development. Its platform, ModelFlow, unifies scientific data, AI and physics-based models, and enterprise workflows, enabling teams to design, simulate, and optimise drug processes with greater speed, accuracy, and scalability than traditional experiment-heavy methods.
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