Domino Data Lab, provider of the Enterprise AI platform, has announced continued life sciences traction and new capabilities to bridge a critical industry gap.
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Six of the top 10 pharmaceutical companies now use Domino to power AI and analytics across discovery, development, and manufacturing. Domino Apps, introduced at RevX —the company’s flagship industry event— help teams deliver the last mile of AI by turning models into interactive applications that put insights directly in the hands of scientific and clinical stakeholders.
Over the past year, Domino Cloud’s annual recurring revenue in life sciences has grown more than twelvefold, driven by increased enterprise adoption across pharma and biotech. Part of its recent industry momentum includes Domino's strategic collaboration with UCB aimed at modernising a Statistical Computing Environment (SCE).
“At UCB, we believe that innovation thrives through collaboration, and this partnership exemplifies our commitment to evolving and pushing the boundaries of what’s possible in life sciences. By harnessing advanced technologies to modernise our statistical computing environment and combining our expertise with Domino’s cutting-edge platform, we aim to accelerate the development of innovative therapies for people living with severe diseases,” said Mike Branson, SVP, head biometrics and data science, UCB. “This new SCE will embed critical metadata and workflow management to enable searchable, responsible data re-use, accelerating our cutting-edge Data Science and enabling more efficient drug development and evidence-based decision making.”
“The world’s top pharma companies are using Domino to get therapies to patients faster,” said Nick Elprin, co-founder and CEO of Domino Data Lab. “We’re proud to help transform how data science powers clinical breakthroughs.”
Turning models into business impact with Domino apps
Domino Apps give data science teams a faster way to deliver their work as interactive applications that stakeholders can explore and act on, without the delays of traditional deployment workflows. These apps deliver the last mile of AI by bridging the gap between technical work and decision-making, helping R&D, clinical, and operational teams interact with models, visualise results, and make faster, more informed decisions.
Whether used to visualise clinical trial enrolment metrics or assess patient eligibility, these apps help teams deliver timely and trusted insights across the development lifecycle. With intuitive URLs, built-in governance, and centralised management, IT teams gain the oversight they need while scientists stay agile.
“Today’s data science doesn’t end with a model, it ends with a user experience,” said Chris McSpiritt, vice president of life sciences strategy at Domino Data Lab. “With Domino Apps, scientists can publish apps in minutes without DevOps or IT bottlenecks, so business users can explore insights, interact with models, and make faster, more informed decisions."
Powering the future of regulated data science
The launch of Domino Apps marks the latest in a series of strategic advances to support the evolving needs of life sciences R&D. Domino continues to partner with industry leaders through the SCE Coalition to co-design a modern analytics workspace, while also contributing to new standards and greater interoperability through its work with Pistoia Alliance and PHUSE.
“We’ve seen a clear shift. Life sciences organisations want a single, unified platform that combines scientific computing, governance, and enterprise-ready AI infrastructure,” added McSpiritt. “Domino is delivering exactly that.”
Making reuse and compliance seamless
Domino continues to enhance its support for 21 CFR Part 11 compliance with features that enable GxP workflows, including electronic signatures, session controls, and unified audit trails. These capabilities help reduce manual compliance burdens and improve traceability across projects, datasets, and applications as part of our ongoing platform improvements.
The release also introduces Smart Flow Exports, which automatically register Flow outputs such as SDTM datasets or model files into Domino Datasets. This streamlines handoffs, preserves lineage, and ensures validated results are reused consistently across teams and trials.