Atlas Bio Raises $12.5M Funding to Replace Phase II Drug Trials With 12-Week AI Simulations

Atlas Bio, the Basel-based tech-bio startup using AI to simulate clinical trials, has emerged from stealth with $12.5 million in total funding**. The round includes a **$10 million seed led by Creandum, alongside a previously unannounced $2.5 million pre-seed led by First Momentum Ventures and LEA Partners . The company did not disclose its valuation.

The funding comes as Atlas Bio prepares to scale its AI foundation model, which simulates patient-level drug responses to de-risk Phase II trials—the stage where approximately 70% of drug candidates fail . The company is already working with a top-10 global pharma company and has helped at least one Basel biotech eliminate animal testing from a specific development workflow .

Atlas Bio Funding Overview By CREDX Media

Startup Name	Atlas Bio
Founders	Simon Romanski (CEO), Sebastian Bayer (CTO), Dominik Hartl (CSO)
Founded	2024
HQ	Basel, Switzerland (incorporated in Delaware)
Round	Seed Funding
Amount Raised	$12.5M total ($10M Seed + $2.5M Pre-Seed)
Valuation	Not disclosed
Category	TechBio, AI Drug Discovery, Clinical Trial Simulation
Lead Investor	Creandum (Seed)
Key Investors	First Momentum Ventures, LEA Partners
Total Funding Raised	$12.5M
Model Training	~2 million human RNA readouts
Simulation Time	12 weeks (vs. 2-3 years for Phase II)
Key Customers	Top-10 global pharma, unnamed Basel biotech

💡 Use of Funds

• Team Expansion: Growing scientific and commercial teams to support platform validation and customer onboarding .
• Platform Validation: Deepening the evidence base for the AI model’s predictions ahead of broader regulatory and commercial adoption .
• Enterprise Customers: Onboarding additional pharmaceutical clients through 2026 .

Startup Overview: Atlas Bio

What Atlas Bio Is Building in Simple Words

Atlas Bio has built an AI foundation model that simulates how individual patients will respond to drugs before a Phase II trial ever begins. The model is trained on roughly 2 million human RNA readouts—molecular signals showing how genes switch on and off in human tissue. By learning these patterns, it can predict drug efficacy and safety across a virtual cohort of patients, compressing what takes two to three years and over $100 million into a 12-week simulation .

• Virtual Patient Cohorts: Simulates how different patient profiles respond to a drug candidate, allowing sponsors to identify likely responders before committing to a narrow trial design .
• Multi-Disease Testing: Because the trial is virtual, the same patient profile can be tested against multiple treatments and diseases simultaneously—something physically impossible in a real clinical trial .
• Animal Testing Replacement: The model aims to replace the animal studies that typically precede human trials. The FDA Modernization Act 2.0 (2022) removed the legal requirement for animal testing, and the FDA has continued issuing guidance on non-animal alternatives .
• Proven Commercial Use: A Basel-based biotech has already used Atlas Bio’s simulation output to proceed directly to human trials without animal testing for a specific drug workflow—one of the first commercial uses of AI simulation as a direct substitute for preclinical studies .

Why Atlas Bio Matters

1. Phase II Is the Drug Industry’s Graveyard Phase II trials cost more than $100 million and take two to three years, yet around 70% of drugs fail at this stage . Atlas Bio’s thesis is that many of these failures aren’t because the molecule doesn’t work—it’s because it was tested against the wrong disease or patient population. The company notes that among drugs eventually approved, around 60% turn out to be more effective for a different condition than the one originally targeted .
2. The Founders Bring Unlikely Credentials CEO Simon Romanski worked on autonomous vehicles at KIT, EPFL, and Stanford before turning to drug development, motivated in part by living with Type 1 diabetes . CTO Sebastian Bayer led large language model research at Google and was an early contributor to autonomous driving . The combination of self-driving car engineering and frontier AI research is unusual in biotech—but Atlas Bio argues that the core problems (selecting training data carefully, validating at scale) translate across domains .
3. Regulatory Tailwinds Are Real The FDA’s shift away from animal testing requirements creates an opening for validated computer models. Atlas Bio is positioning its platform to meet that demand, though regulatory acceptance remains evidence-dependent and not automatic .
4. Commercial Traction Precedes Scale Working with a top-10 pharma company and having a biotech use simulation output to skip animal testing are meaningful proof points for a company that launched from stealth just weeks ago .

Founder Background: The Self-Driving Engineers Turned Drug Developers

Simon Romanski (CEO) studied electrical engineering at KIT, EPFL, and Stanford. He was one of the first engineers to work on autonomous vehicles in 2014. His interest in drug development was personal: living with Type 1 diabetes made him attuned to how quickly treatment options can improve—and how most clinical trials fail .

Sebastian Bayer (CTO) worked on perception models for autonomous driving before leading Google’s research on language models for Search, YouTube, and Google Play. His framing of the technical challenge: “Most biological effects are not caused by drugs. To predict unknown drug effects, we need to learn biology in an unbiased way. By training our foundation model on roughly 2 million human RNA readouts, we can generalise across indications and treatments” .

Dominik Hartl (CSO) was a doctoral fellow at the Friedrich Miescher Institute for Biomedical Research and brings over a decade of drug development experience to the team .

Romanski’s framing of the opportunity is direct: “For sixty years, drug development has been built on two flawed assumptions: that mice predict humans, and that the only way to learn whether a drug works is to give it to thousands of patients and wait. Both are now becoming outdated. Human biology is finally legible to AI” .

Founder Intelligence: What Founders Can Learn from Atlas Bio

• Translate skills across domains: Romanski and Bayer came from autonomous driving and Google-scale AI. They didn’t assume the technical stack would transfer directly—but they recognized that the problems (data selection, validation, simulation) were structurally similar .
• Attack the most expensive failure: Phase II is where drug development breaks down financially and scientifically. Atlas Bio focused on the single highest-stakes checkpoint rather than trying to improve the entire pipeline.
• Use regulation as a tailwind: The FDA’s shift away from animal testing created a window for AI simulation. Atlas Bio positioned itself to meet that demand with evidence, not just promises .
• Prove it with one customer before scaling: A Basel biotech using simulation to skip animal testing is a concrete proof point. In biotech, one validated commercial use case is worth more than a hundred theoretical benefits.

Investor Intelligence: Why This Round Gets Premium Capital

Creandum led the $10 million seed. Partner Katharina Wilhelm joined the board. Creandum is an early backer of Spotify and Trade Republic, and its decision to lead Atlas Bio’s round signals conviction that AI simulation can become a standard part of drug development .

First Momentum Ventures and LEA Partners led the $2.5 million pre-seed, backing the company before the current AI simulation narrative gained momentum .

The investor composition signals conviction: Creandum’s track record with category-defining consumer and fintech companies, combined with deep-tech specialists at the pre-seed stage, positions Atlas Bio to navigate both the commercial and scientific challenges of replacing clinical trials with simulation.

The Road Ahead for Atlas Bio

Expansion Targets

With $12.5 million in capital, Atlas Bio will:

• Grow scientific and commercial teams to support platform validation and customer onboarding .
• Deepen platform validation ahead of broader regulatory and commercial adoption .
• Onboard additional enterprise clients through 2026, building on the top-10 pharma relationship .

The Validation Question

Atlas Bio’s model’s value hinges on proving its predictions reliably match human outcomes. Regulatory acceptance remains fit-for-purpose—each use must be validated—and a headline failure-rate claim alone won’t persuade sponsors to abandon real trials. The company’s first trial results are expected within six weeks , which will provide the first external signal of whether the simulations deliver on their promise.

Competitive Response

Expect:

• Other AI drug discovery platforms to explore clinical trial simulation as an adjacency.
• Traditional CROs to build or acquire simulation capabilities as regulatory acceptance grows.
• Atlas Bio’s moat: RNA-trained foundation model + commercial proof points + regulatory timing + founder expertise across AI and drug development.

Conclusion

Atlas Bio’s $12.5 million funding validates the thesis that Phase II’s 70% failure rate is a data problem, not a science problem—and AI simulation can fix it. The company’s model, trained on 2 million human RNA readouts, compresses years of trial work into weeks and has already been used by a biotech to skip animal testing entirely.

Market tailwinds favor Atlas Bio: the FDA’s shift away from animal models, the $100 million cost of Phase II failures, and the willingness of top pharma companies to explore simulation as a complement to real trials. The company’s unusual founder pedigree and early commercial traction position it to test whether virtual trials can become a standard part of drug development.

For deep analysis of TechBio funding, AI drug discovery innovation, and companies reshaping clinical development, explore CREDX Media for comprehensive coverage of startup funding trends, valuation intelligence, and founder playbooks.

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Frequently Asked Questions

1. What is Atlas Bio’s total funding and latest round size?
Atlas Bio has raised $12.5 million total**, including a **$10 million seed led by Creandum and a $2.5 million pre-seed led by First Momentum Ventures and LEA Partners .

2. How much did Atlas Bio raise in its seed round and who led the investment?
Atlas Bio raised $10 million in seed funding led by Creandum, with Partner Katharina Wilhelm joining the board .

3. What is the valuation of Atlas Bio?
Atlas Bio did not disclose its post-money valuation.

4. Who are the major investors in Atlas Bio?
Major investors include Creandum (seed lead), First Momentum Ventures, and LEA Partners (pre-seed leads) .

5. Who are Atlas Bio’s founders?
Atlas Bio was founded in 2024 by Simon Romanski (CEO), a former autonomous vehicle engineer, and Sebastian Bayer (CTO), who led large language model research at Google. Dominik Hartl (CSO) leads scientific strategy .

6. What is Atlas Bio’s business model?
Atlas Bio operates a B2B model, selling simulation services and platform access to pharmaceutical companies and biotechs seeking to de-risk Phase II trials and replace animal testing .

7. How does Atlas Bio’s AI model work?
The model is trained on roughly 2 million human RNA readouts from tissue. It simulates how individual patient profiles respond to drug candidates, enabling virtual trials that test multiple treatments across diseases simultaneously .

8. What traction justifies Atlas Bio’s funding?
Atlas Bio is working with a top-10 global pharma company, and a Basel biotech has used its simulation output to eliminate animal testing from a specific workflow and proceed to human trials .

9. What are Atlas Bio’s plans for the $12.5 million funding?
Atlas Bio will expand its scientific and commercial teams, deepen platform validation, and onboard additional enterprise clients through 2026 .

10. How long does an Atlas Bio simulation take?
Atlas Bio’s simulations take approximately 12 weeks, compared to the two to three years required for a real Phase II trial .

11. Who is the CEO of Atlas Bio?
Simon Romanski serves as CEO and co-founder. He previously worked on autonomous vehicles and was motivated to enter drug development in part by living with Type 1 diabetes .