Benchling and Lilly TuneLab Join Forces to Enhance AI Accessibility for Scientists Globally

Benchling Partners with Lilly TuneLab: A New Era of AI Accessibility for Scientists

In a groundbreaking move that promises to reshuffle the landscape of biotechnology research, Benchling, a platform dedicated to accelerating scientific progress, has announced a strategic partnership with Lilly TuneLab, an innovative AI and machine learning platform developed by Eli Lilly and Company. This collaboration is set to provide scientists with unprecedented access to advanced AI models trained on data amounting to over $1 billion, drawn from decades of extensive research by Lilly.

Transforming Scientific Workflows

The collaboration will primarily benefit Benchling’s customer network, which consists of more than 1,300 biotechnology companies, enabling them to seamlessly incorporate advanced AI predictions into their daily workflows. Benchling allows scientists to utilize Lilly's pre-trained AI models for various applications, including antibodies and small molecule predictions, enhancing both the efficiency and accuracy of their research processes.

Sajith Wickramasekara, CEO and co-founder of Benchling, emphasized the strategic nature of this partnership, stating, "Lilly selected Benchling as a key platform partner for TuneLab due to our central role in the daily operations of scientists. Through this collaboration, we are laying the groundwork for sophisticated AI tools that can be accessed throughout the biotech industry."

The integration of Lilly TuneLab within Benchling’s platform also represents a crucial effort to address the common challenges faced by organizations in the biotech field, particularly in adopting AI-driven methodologies. According to Benchling's 2026 Biotech AI Report, data quality, availability, and the lack of secure infrastructure were cited as primary reasons for many AI initiatives failing.

Bridging Gaps in Biotech AI Adoption

Despite the rapid adoption of AI in biotech, statistics indicate that a significant proportion of organizations struggle to efficiently leverage high-quality datasets necessary for scaling AI efforts. Notably, only 21% of emerging biotech companies commit to sharing data through industry consortiums. The partnership between Benchling and Lilly aims to bridge this gap, providing a robust solution that empowers companies of all sizes to benefit from models trained on Lilly’s proprietary datasets.

The Impact of TuneLab

Launched just a year ago in September 2025, Lilly TuneLab boasts models backed by an extensive array of research data pertaining to drug disposition, safety, and preclinical trials involving countless unique molecules. The addition of these models into Benchling’s platform not only enhances the offerings available to researchers but also establishes Benchling as a prime venue for accessing advanced AI tools alongside organized datasets. The collaboration with other key players like Anthropic and NVIDIA further solidifies Benchling's status as a leader in AI deployment for the life sciences sector.

As we move through 2026, Benchling customers will soon find themselves equipped to opt into this innovative program and employ Lilly's proprietary models at will, streamlining their scientific workflows and potentially leading to faster innovation cycles.

In an era where collaborations like this may be crucial for progress, the Benchling and Lilly TuneLab partnership represents a significant step toward democratizing access to transformative AI technologies in scientific research.

For those eager to explore more insights, the complete 2026 Biotech AI Report can be accessed at benchling.com/biotech-ai-report-2026.

About Benchling

Founded in 2012, Benchling’s mission is to unlock the full potential of biotechnology. It provides a unified, cloud-based platform that is trusted by over 1,300 biotech companies globally, spanning from pioneering startups to major biopharmaceutical firms like Merck and Moderna. Benchling’s solutions help scientists capture, connect, and analyze data throughout the research and development lifecycle, integrating seamlessly with AI models that enhance their productivity and research outcomes.