Ginkgo Datapoints has partnered with Lilly TuneLab to strengthen AI drug discovery through high-quality biological data generation. The new agreement connects Ginkgo’s biological data services with TuneLab’s collaborative AI/ML drug discovery platform. Ginkgo Bioworks announced the agreement through its Ginkgo Datapoints offering. TuneLab was created by Eli Lilly and Company to support collaborative drug discovery.
The platform helps participating biotech companies access AI/ML models. These models use decades of Lilly’s proprietary research data. Through the partnership, Ginkgo Datapoints will provide discovery data generation services. These services include small molecule developability and antibody developability testing. The companies aim to connect AI-generated hypotheses with validated experimental results. This connection can help biotech teams move from digital design to laboratory validation faster.
“The combination of Ginkgo’s automation and TuneLab’s collaboration platform aims to accelerate next-generation therapeutic development across the industry. TuneLab is attracting many biotechs who benefit from access to powerful predictive models that are constantly improving via contributions from the network. Layering on Ginkgo’s ability to quickly generate high-quality, AI-ready datasets at any scale, we believe TuneLab participants will be able to close the loop between digital design and biological reality faster than ever before,” said John Androsavich, PhD, General Manager at Ginkgo Datapoints.
Ginkgo Datapoints Brings Automated Lab Data to TuneLab
The partnership gives TuneLab biotech participants access to advanced laboratory automation. Ginkgo’s technology can run experiments without relying on manual bottlenecks. This capability can support faster data generation for AI-driven drug discovery programs. Ginkgo will provide antibody and small molecule developability screening services. The company can also deliver results in machine-learning-ready formats.
These formats can connect directly with TuneLab’s predictive AI/ML models. As a result, researchers can move between computational design and laboratory testing more efficiently. The process can turn an AI-generated hypothesis into validated lab data within days. Previously, similar workflows could require several months to complete. The integration therefore addresses an important challenge in AI drug discovery.
AI models require reliable experimental data to refine their predictions. In contrast, biotech companies need efficient ways to validate AI-generated hypotheses. Ginkgo Datapoints and TuneLab want to close both sides of this equation. The collaboration merges automated biological data generation and predictive models.
It also allows for a more connected approach to modern drug discovery. Ginkgo’s laboratory automation can help reduce delays in experimental workflows. At the same time, TuneLab provides participating companies with access to predictive AI/ML capabilities. These capabilities can help researchers to more efficiently evaluate possible treatment opportunities.
Standardized Data Can Improve AI and ML Models
Ginkgo will also offer standardized assay protocols to the whole TuneLab ecosystem.
Standardization can help make experimental data more uniform. That consistency in learning from new information can support AI/ML models. The companies believe this will increase the overall impact of the TuneLab platform. It can also help participating firms to better exploit biological datasets generated. New TuneLab members now have access to data generated by datapoints. That data can be used to derive further benefits from the platform’s models.
This provides a feedback loop between experimental data and predictive models. The participating models can continue to improve as more data enters the ecosystem. This is consistent with the growing importance of artificial intelligence and machine learning in the domain of biotechnology. It also points to the need for high-quality datasets in pharmaceutical research. Access to robust experimental data can accelerate the validation for biotech companies.
Also, standardized testing can make data easier to use in AI-driven workflows. The partnership provides another developability research resource to TuneLab participants. It also broadens the range of laboratory services that are available through the collaborative ecosystem.
Partnership Expands Ginkgo Datapoints’ AI Strategy
The agreement is another expansion of Ginkgo Datapoints’ work together. The company continues to develop services tailored for AI/ML applications. Its approach is to link predictive models with experimental data from the real world. Ginkgo can produce biological data at scale for biopharma organizations. This ability is assisting companies developing AI-based approaches to drug discovery.
The collaboration with TuneLab is another link between computational research and laboratory experiments. It also supports Ginkgo’s broader work with companies throughout the biopharma industry. Those partnerships are also helping Ginkgo to share experimental data more widely. The company can also help organizations embed biological testing into AI workflows.
This approach is in keeping with the growing use of AI in pharmaceutical research and development. Reliable laboratory evidence and mathematical models are both necessary for AI drug discovery programs. Combining these capabilities, therefore, can help researchers build more complete workflows. The TuneLab partnership gives us a framework for integrating both sets of resources. It also provides biotech participants with access to Ginkgo’s automated testing capabilities.
TuneLab Supports Broader Biotech Innovation
Lilly TuneLab operates as part of Lilly Catalyze360. The broader initiative also includes Lilly Ventures, Lilly Gateway Labs, and Lilly ExploR&D. Together, these groups support biotech innovation through several resources. They provide access to strategic capital and laboratory space. They also offer technology and research and development capabilities. TuneLab adds AI/ML drug discovery resources to this broader support ecosystem.
Participating biotech companies can use predictive models trained on Lilly’s research data. They can also generate additional experimental data through Ginkgo Datapoints. This creates opportunities to connect AI-based predictions with laboratory findings. The partnership further strengthens the role of biological data within AI drug discovery. It also supports the development of more integrated workflows for therapeutic research.
And as AI adoption spreads across biotechnology, data generation remains a key element. Experimental evidence is required to validate and refine computational predictions. Ginkgo Datapoints adds automated data generation to TuneLab network At the same time, TuneLab provides participating biotech companies access to AI/ML models.
This partnership joins these capabilities together with a common drug discovery workflow. Ultimately, the collaboration is designed to help researchers accelerate the pathway from AI-generated ideas to lab validation. It also shows how AI, machine learning, automation and biological data can be merged. Ginkgo Datapoints and TuneLab are expanding access to connected drug discovery capabilities under the agreement. Companies are linking predictive models more closely to real-world experimental data.
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News Source: Businesswire.com
