Helical, a startup developing an AI-powered virtual lab for pharmaceutical research, has successfully closed a $10 million seed funding round. The investment was led by redalpine with participation from Gradient, BoxGroup, and Frst. Founded in 2024, the company aims to solve a critical throughput problem in drug discovery by bridging the gap between computational models and reproducible biological validation.
Addressing the Drug Discovery Bottleneck
The pharmaceutical industry faces immense pressure from rising R&D costs, which now exceed $300 billion annually. Timelines for bringing a new drug to market often stretch beyond a decade, with failure rates for clinical trial candidates surpassing 90 percent. This inefficiency highlights a critical need for new approaches to streamline the discovery process and improve its success rate.
While biological foundation models offer a promising way to test hypotheses computationally, a significant gap remains in their practical application. Pharma teams often struggle to translate model outputs into actionable scientific decisions due to fragmented workflows. This results in siloed efforts between machine learning engineers and bench scientists, hindering progress and reproducibility across programs.
A Virtual AI Lab for Reproducible Science
Helical addresses this challenge with its virtual AI lab, an application layer designed to make in-silico science reproducible at an industrial scale. The platform features two interconnected surfaces: a Virtual Lab for biologists and a Model Factory for data scientists. By operating on shared data and models, it fosters seamless collaboration and supports evidence-based decision-making throughout the discovery process.
Co-founder Rick Schneider emphasizes that discovery is driven by systems that operationalize model insights, not by models alone. Helical’s platform is built to turn powerful models into validated workflows that scientists can run, trust, and defend. This systematic approach aims to reduce the time from an initial hypothesis to a final decision from months to mere days.
Demonstrating Early Industry Adoption
The company is already demonstrating significant traction, with deployments at several top-20 global pharmaceutical companies. Public collaborations include work with Pfizer on predictive blood-based safety biomarkers and another with Tanabe Pharma. These early partnerships have successfully compressed discovery timelines from years down to several weeks, showcasing the platform's immediate impact.
Beyond speed, the platform’s core focus is on making scientific decisions defensible and reproducible. This is increasingly critical as regulators like the FDA heighten expectations for transparency in AI-driven discovery. Helical's system provides the structured framework necessary for AI in biology to scale reliably and responsibly within large organizations.
Strategic Investment and Future Vision
The new funding was supported by notable angel investors, including the CEOs of Cohere and HuggingFace, alongside the institutional funds. Daniel Graf, General Partner at redalpine, expressed strong confidence in Helical's ability to build the essential AI orchestration platform for the industry. He highlighted the unique convergence of biological foundation models and general language reasoning models as a key opportunity.
This infusion of capital will enable Helical to deepen its deployments across more therapeutic areas with its existing clients. The company also plans to expand its engagement with other major pharmaceutical organizations. A key focus will be the continued development of the platform’s compounding evidence layer to improve performance and reliability across diseases.
Helical's successful funding round marks a significant milestone in the evolution of AI-powered drug discovery. By providing a system that prioritizes reproducibility and collaboration, the company is not just building another model but a comprehensive solution. This approach positions Helical to address the industry's core challenges of cost and efficiency, potentially accelerating the delivery of new medicines to patients.