Nova in Silico

Nova in Silico

Paris, France· Est.
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Private Company

Total funding raised: $1.8M

Overview

Nova In Silico is a Paris-based innovator providing an in silico clinical trial simulation platform named jinkō. The platform leverages knowledge-based mechanistic modeling to create digital twins of diseases and treatments, enabling pharmaceutical companies to simulate and optimize trial protocols before human testing. This approach seeks to derisk development, accelerate timelines, and improve market access strategies. The company collaborates with major industry players like Takeda and Fujitsu, positioning itself as a key enabler of digital transformation in clinical research.

AI / Machine LearningDrug Discovery

Technology Platform

jinkō, a collaborative clinical trial simulation platform using knowledge-based mechanistic modeling to create disease models, virtual patient populations, and treatment models for in silico trial prediction and optimization.

Funding History

1
Total raised:$1.8M
Seed$1.8M

Opportunities

The rising cost and high failure rate of traditional clinical trials create a massive market for simulation technologies that can derisk development.
Increasing regulatory openness to model-informed drug development and digital evidence provides a tailwind for adoption.
Partnerships with large technology firms like Fujitsu can accelerate platform capabilities and market reach.

Risk Factors

Regulatory acceptance of in silico data as primary evidence for drug approvals remains limited and is a key adoption hurdle.
The company faces intense competition from larger CROs, software firms, and other AI-driven startups.
As a young platform company, its commercial success depends on convincing traditionally conservative pharmaceutical clients to change established R&D workflows.

Competitive Landscape

Nova In Silico competes in the growing market for clinical trial simulation and model-informed drug development. Key competitors include large contract research organizations (CROs) offering modeling services, established simulation software companies like Dassault Systèmes, and a proliferating number of AI/ML-focused drug discovery startups. Its differentiation lies in its collaborative, knowledge-based mechanistic modeling approach versus purely data-driven AI.