Enabling the Lab-in-a-Loop
Unifying Science, Data, and Decision-Making to Drive Breakthroughs
4 Key Customer Questions
What key life science industry dynamics and trends should shape our digital strategies
What emerging technologies and innovations will enable life science transformation
What data, analytics and AI strategies can organizations deploy to maximize business outcomes
How do we respond to rapidly changing business needs through a scientific intelligence platform architectural approach?
Unlocking Future Life Sciences Breakthroughs
Breakthroughs in science don’t happen in isolation— they’re the result of bold ideas, rapid iteration, and fluid collaboration across people, data, and tools. But today’s research labs are stuck fighting yesterday’s battles. Fragmented workflows. Disconnected data. Siloed systems that can’t keep up.
long been estimated a recent study
It has that scientists lose upwards of 50 days per year owing to inefficient processes, and on average 10 to 20 percent of development work is repeated due to data integrity and accessibility issues.1 In , 80% of scientists said that the workarounds currently required to get data into meaningful outputs are negatively impacting their work and almost 70% reported compromised decision-making because of this.2
As the life sciences race into increasingly complex territory—cell and gene therapies, mRNA vaccines, multispecific antibodies—the sheer volume and variety of research data are exploding. Yet most labs are still relying on legacy systems that weren’t built for this kind of scale or complexity. To stay competitive, accelerate innovation, and drive impactful discoveries, life sciences companies need more than incremental improvements. They need a new foundation, one that unifies data, adapts to evolving science, and connects every team and tool across the full R&D lifecycle: Design. Make. Test. Decide.
Dotmatics Luma
That’s the promise of . It doesn’t just modernize R&D, it reimagines it as a lab-in-a-loop— an agile, intelligent ecosystem that accelerates discovery and empowers researchers to make faster, more informed decisions.
Why a Lab-in-a-Loop?
A lab-in-a-loop is an AI-powered system that links real-time experiments with machine learning (ML) to accelerate drug discovery. Data from each test guides the next step, creating a fast, self-improving cycle. This closed-loop approach streamlines design, testing, and optimization—cutting costs, saving time, and increasing success rates.
The Innovation Bottleneck
Why Life Sciences Can’t Afford Fragmented Data and Disconnected Teams
Despite big ambitions, most life sciences organizations are stuck navigating outdated systems that make collaboration harder and breakthroughs slower. The result? Slowdowns, missed insights, and costly rework. These obstacles affect productivity in both wet and dry lab environments, with the most pressing challenges spanning a few critical categories:
Complexity in Disease-Focused Research (Multimodal Approaches)
Increasingly researchers addressing complex diseases (e.g. small molecules, biologics, gene therapy, and cell therapy)3. Each requires different groups and skillsets, with varying needs. This makes it difficult to create a single platform that can effectively support all approaches. rely on multiple therapeutic modalities
Lack of Comprehensive Scientific Traceability and Data Connectivity
Scientists need more than just data—they must also track the evolution of scientific thinking, hypotheses, and iterative analyses. They need a “digital thread” that begins by connecting all data sources that can be used in R&D. Existing systems often fail to capture these critical aspects, leaving gaps in the lineage of experiments, workflows, and conclusions. Procedural steps are often disconnected from sample data, materials, and instrument results in LIMS, making it difficult to reconstruct the full scientific process. This fragmentation hinders reproducibility, complicates regulatory compliance, and limits AI-driven insights. As a result, scientists are left piecing together information outside their workflows. That leads to uninformed decision-making rather than efficient, insight-driven research.
Disconnected Wet Lab and Dry Lab Workflows
Fragmented workflows between wet and dry labs cause significant inefficiencies, especially during data handoffs. Manual processes such as spreadsheets and email introduce delays, data loss, and reproducibility challenges. Without seamless integration between experimental and computational systems, dry lab teams often receive incomplete or poorly annotated data, leading to time-consuming reformatting. This disconnect hinders collaboration between biologists, chemists, and computational scientists, impeding the sharing of insights and slowing discovery.
Critical Need for a Data-First Approach to AI in Life Sciences
Gartner predicts that by 2027, non-technologyrelated reasons, such as misaligned processes, will cause 40% of AI project failures in life sciences.4 AI and ML depend on high-quality, structured, and interoperable data. Yet many organizations struggle with poor data hygiene, inconsistent ontologies, and fragmented datasets, making it hard to train and validate effective AI models.
, life sciences organizations need tools that support automated data harmonization, lineage tracking, and seamless integration across experimental and computational workflows. To enable AI- driven insights
What is a Digital Thread?
A digital thread is a connected workflow of data across the entire lifecycle involved in developing new therapeutics, from early research and development to full scale production. It connects traditionally siloed functions — like design, development, testing, manufacturing, and maintenance — into a single, cohesive data flow. It's the digital backbone that ensures traceability and consistency of data from end to end. That means companies can innovate faster, accelerate market introduction of new therapies and most importantly, be able to improve people’s lives.
The Limitations of Existing
Software—and the Opportunity to Evolve
Scientific organizations today rely on a range of software solutions—Electronic Lab Notebooks (ELNs), Laboratory Information Management Systems (LIMS), and Scientific Data Management Systems (SDMS)—to manage data, document experiments, and enable collaboration. These tools have played an essential role in digitizing scientific workflows, and for many teams, they continue to serve core needs. However, they were never designed to provide the real-time data connectivity, cross-functional intelligence, or AI-powered insights that are increasingly central to modern R&D.
Rather than replacing these systems, there is an opportunity to build on their strengths by integrating them into a unified, intelligent framework that supports multimodal scientific workflows and future-ready discovery.
ELNs: Flexible But Fragmented
Originally designed to support domain-specific needs— particularly in chemistry—ELNs have proven invaluable for documenting experiments and enabling collaboration. However, they were not built to manage structured, real-time data or integrate seamlessly with AI and automation. For many research teams ELNs remain foundational, but their value can be significantly extended through platforms that offer advanced analytics, experimental traceability, and intelligent data flow across teams.
LIMS: Structured Data, Disconnected Science
LIMS provide strong structure and excel at tracking samples and results, particularly in automated lab environments. However, they lack an inherent concept of an experiment, making it impossible to map scientific workflows in a way that reflects real-world lab processes. LIMS can associate large datasets with sample IDs, but provide no context for what those samples represent, nor can they trace the upstream steps that may have influenced results.
SDMS: Data Repositories Without Context
Similarly, SDMS systems, designed for managing and archiving scientific data, play a crucial role in centralizing instrument outputs and raw data files. However, they primarily serve as repositories rather than active workflow management tools. They lack the ability to contextualize data within the broader scientific process, making it difficult to correlate raw instrument data with experimental decisions and outcomes. Despite SDMS ensuring data retention and compliance, they do not inherently support real-time data connectivity or workflow-driven insights.
To enable agile innovation and seamless scientific collaboration, digital workflows must allow tasks and data to move fluidly between traditionally siloed systems—adapting to evolving research needs and enabling AIdriven discovery. This is where Luma comes in—not as a replacement, but as a bridge to the future.
Simplifying Scientific Discovery with Powerful a Online ELN
ELNs continue to transform how science happens. They make it easier to capture, organize, and share data across scientific disciplines, With flexible templates, integrated workflows, and real-time collaboration, they cut down busywork and speed up discovery. In an era of complex, fast-paced research, ELNs are more than a tool—they’re a foundation for doing reliable, collaborative, and impactful science.
A New Market for Multimodal Scientific Intelligence Emerging Market Alert
Life Science organizations are demanding a new category of software designed specifically to address both current and future challenges: Multimodal Scientific Intelligence Platform.
According to Gartner, by 2027, nearly 75% of life science organizations will adopt cloud-based composable architectures to power AI-driven research. These organizations are preparing for a world where the drug discovery cycle—from preclinical through commercialization—takes less than seven years. Other companies report that using AIdriven approaches they have already compressed the discovery phase down to from 9 to 18 months.5
The message is clear: science needs to move faster, and data needs to move with it. But today’s software forces scientists to make a difficult choice: move quickly and risk accuracy, or slow down to ensure precision. Rarely can they do both.
With Luma, data doesn’t just support research—it actively drives it.
In this new era of data-driven science, teams need a platform that unifies all aspects of the research lifecycle, allowing for seamless data flow, adaptive workflows, and crossfunctional collaboration. This is where the Multimodal Scientific Intelligence Platform comes in, a new category of software designed specifically to address these challenges. By linking every step of the research cycle—Design, Make, Test, Decide—and ensuring that the inputs and outputs at each step are connected and accessible in real-time, the Multimodal Scientific Intelligence Platform unlocks the full potential of research data.
In this emerging market, Dotmatics Luma is the first of its kind. Luma represents the next logical step in scientific R&D, designed to future-proof organizations by integrating the best capabilities of ELNs, LIMS, and SDMS into a unified, intelligent platform. This isn’t just better software—it’s a new way of working. One that makes AI possible, cross-functional collaboration natural, and innovation scalable.
Luma is built specifically to bridge the gaps that traditional systems leave behind. This flexibility empowers scientists to explore multiple paths simultaneously, pivot based on emerging data, and standardize best practices across programs and teams. It’s also purpose-built to interoperate seamlessly with any existing system, from ELN to LIMS to SDMS, enhancing their capabilities through real-time data connectivity, intelligent workflow orchestration, and cross-functional insights.
With Luma, scientific organizations can protect their existing investments while unlocking new levels of efficiency, collaboration, and discovery—at their own pace and on their own terms. That means no longer choosing between speed and accuracy. Decisions become faster because they’re smarter, and collaboration isn’t a workaround, it’s built in by design.
Introducing Luma Double-Click: What Makes Luma So Different?
Luma isn’t just a platform—it’s a new architecture for scientific discovery.
Luma enables researchers to model all of their processes—including both dry lab and wet lab operations—as a cohesive digital environment. Every research step is precisely tracked and can be adapted in real time, without relying on complex service-based configurations. Scientists create and update workflows on their own, building a “digital twin” of their end-to-end workflows with greater precision than a typical ELN and more flexibility than LIMS tools.
Luma maintains relationships between material inputs and outputs across steps and automatically links raw and processed data (including simulation inputs and outputs). This creates a detailed, connected record of end-to-end scientific processes, supporting faster, data-driven decisions across therapeutic areas. Plus, these capabilities are delivered without compromising record-keeping and compliance requirements. Data with high levels of detail can also be exported to ELNs in a compatible format reducing the burden of duplicate entry and supporting downstream requirements for IP documentation and compliance.
Each therapeutic mode comes with its own set of unique complexities, from living cells and multiple protein formats to RNA, small molecules, and their combinations. The workflows required to make, validate, and purify these materials are complex, with some processes involving over 100 steps. Luma takes a different approach, delivering a platform that adapts gracefully across scientific modes and domains. Its combination of adaptive process modeling and data automation allows it to scale with the demands of multimodal science—bridging gaps with a precision that ELNs and LIMS were never designed to address. By unifying fragmented research processes into a connected, data-driven environment, Luma is purpose-built for the complexity of modern R&D. It adapts to your workflows, scales with your needs, and evolves as your research advances.
The result? Luma transforms research from fragmented to fluid. It’s not just a tool—it’s how modern science moves forward.
& Ontology Management Data Management & Processing Adaptive Workflows
Scientists Deserve Better:
Luma Redefines What’s
Possible in R&D
How does Luma set a new standard in scientific research? By combinin precision, adaptability, and comprehensive traceability, Luma empowers research teams to achieve faster, more accurate, and more efficient results
1 Seamless Integration with Industry-Leading
Apps
Seamlessly Inte rates with Apps Scientists Lov
GeneiousGraphPad PrismSnapGeneOMIQFCS ExpressBioGlyphProtein Metrics
Luma is desi ned to maximize the value of Dotmatics suite of industry-standard applications, includin , , , , , , and ach tool is developed and co-desi ned by Dotmatics, ensurin in-depth understandin of both use cases and user needs, and seamless inte ration into the Luma platform No other platform offers this level of inte ration with a wide ran e of co-developed solutions, which allows tools to work to ether harmoniously and efficiently
Plus, Luma s open framework supports deep inte rations with any existin technolo y, whether third-party applications or in-house solutions That means research teams et the flexibility to continue usin the technolo ies they know and trust, while benefitin from a unified, automated platform Luma also provides compatibility across diverse IT ecosystems to minimize fra mentation and foster a seamless flow of data across all the tools scientists use
2 Adaptive Task-Based Workflows for Multimodal Research
Luma’s adaptive workflow system represents a significant departure from rigid, process-centric approaches. Rather than imposing a linear, step-by-step structure, Luma empowers teams to dynamically assign and adjust tasks as the needs of the research evolve. This task-based, action-centric approach aligns data capture and decision making with how research actually progresses in real-world settings. Key features of Luma’s adaptive workflows include:
Flexible Task Assignment
Luma assigns tasks dynamically to the appropriate people, instruments, or automated systems, allowing research teams to remain agile and responsive to new insights and shifting priorities. There’s no need to follow a rigid sequence, making it easier to adjust as the research progresses.
Contextualized Data Capture
Tasks are specifically aligned with the goals of the research, ensuring that data captured reflects changes, optimizations, and alterations across different research modalities (e.g., protein therapeutics, gene therapy). This ensures that the right information is captured in context at every stage.
Low-Code App Building Offers Real-Time Adaptation
As new insights or challenges arise, Luma’s workflows allow for iteration and refinement through low-code capabilities that help teams quickly adapt when data needs change and usecases evolve. This adaptability is essential for iterative tasks like experimental design or assay development, where unexpected challenges or insights are common.
Seamless Integration Across Modalities
Whether working on antibody discovery or cellbased therapies, Luma’s flexible, multimodal framework enables easy collaboration. Teams can share insights and data without the bottlenecks that arise from rigid, siloed workflows.
3 Traceability Across the Entire Biologic Design Cycle
Legacy systems often fail to handle the complexity of modern biologic formats like multispecific antibodies (MsAbs), leading to imprecise data representation and fragmented workflows. These systems struggle with tracking molecular structures, creating gaps in traceability, which can cause miscommunication, delays, and costly errors.
Luma solves these issues by providing accurate molecular registration and full lifecycle traceability. Each molecule—whether in design, production, or testing—is assigned a unique ID, ensuring seamless tracking across every stage of the biologic discovery process. This comprehensive traceability helps teams to maintain data integrity from the initial design phase through to production, reducing miscommunication and errors.
4 Multimodal Self-Service Agility
The Luma platform empowers researchers to easily configure workflows, tasks, data models, and governance settings to suit evolving research needs—no heavy IT resources or outside consultants required. It's simple and fast to build new workflows or adjust existing ones on demand, seamlessly adapting to research projects. Users can choose which modalities they want to work with, and they can explore and modify that data on their own. This self-service approach lets scientists and researchers modify processes mid-experiment without disruption, ensuring workflows are as dynamic as the discoveries. They can design personalized interfaces with drag-and-drop functionality for charts, dashboards, and scientific visualizations, enhancing usability and decision support.
Plus, Luma’s domain-specific AI accurately predicts scientific outcomes. From auto-gating in flow cytometry to other advanced predictions, Luma transforms complex data into reliable insights. Researchers can natively combine data across scientific disciplines with Luma’s integrated multimodal AI to predict and simulate complex outcomes, optimizing processes like antibody efficacy and developability.
Introducing the Solutions & Products
Bridging Modalities to Advance Therapeutic Discovery
Scientists want to pick the best therapy or combination of therapies to address a particular target by researching and testing across multiple domains of science
Antibody Discovery & Protein herapeutics: monoclonal or multispecific antibody researc
Cell herapeutics: Stem and CAR-T therapie
Gene herapeutics: CRISPR, Zinc Fingers, and TALEN
Vaccines: mRNA based vaccine
Oligotherapeutics: including siRNA
Luma’s vision is to offer custom-built Solutions for the R&D laboratory to enable whichever therapeutic research modality or combination of modalities you’re pursuing, including MsAbs, CAR-T cells, and ADCs. Each of Luma’s Multimodal Solutions will leverage capabilities from Dotmatics’ best-in-class scientific software—from GraphPad Prism to Protein Metrics to Geneious. Plus, each will be composed to work harmoniously within the Luma platform—to eliminate data and process silos, and enhance the overall functionality of the tools.
The Right Tool for Every Scientific Function
As part of the Luma Platform, Dotmatics offers research organizations the following Luma products individually focused on the scientific function required:
BioGlyph Luma is a next-generation protein design and engineering solution purpose-built for the complexity of modern biologic R&D. Supported capabilities include asynchronous, highly scalable registration of interrelated entities, delivering traceability and information depth across the design process. Scientists create and update workflows on their own, building a ‘digital twin’ of their end-to-end workflows with greater precision than existing tools available today. Empowers teams to create, register, and analyze diverse protein designs—including multispecific antibodies and fusion proteins—with precision, accuracy, and flexibility. Leverage AI and molecular dynamics simulations to assess stability, manufacturability, and developability—optimizing complex biologic modalities before they reach the lab.
Geneious Luma enables researchers to use the advanced bioinformatics, molecular biology, and antibody discovery capabilities of Geneious Prime and Geneious Biologics to annotate and filter sequences and to design constructs. Then, seamlessly working together with Luma, researchers can execute their cloning, expression, and purification tasks using Luma’s Adaptive Workflow capabilities.
Designed to help scientific teams collect, manage, and use data from various sources, including instruments, scientific applications, and files. Luma Lab Connect links to any lab instrument, quickly extracting and parsing raw data and metadata and converting to analyzable structured representations.
OMIQ Luma brings advanced high-dimensional flow cytometry analysis into the Luma platform, enabling teams to harness powerful cloud-native clustering, dimensionality reduction, and automated gating within their preferred OMIQ environment. Whether you re running complex immunophenotyping or exploring novel populations, OMIQ continues to deliver the depth of analysis researchers rely on— now with a seamless connection into Luma. That means your data, annotations, and insights become part of a unified, AI-ready ecosystem accessible across your organization.
Prism Luma leverages GraphPad Prism, the industry-leading data analysis software, for antibody assay development and screening. This product includes native normalization and preprocessing of results prior to analysis in Prism (mitigating the need to use Excel) and contextual linking between assay results, registered materials, and experimental context.
FCS Express Luma enhances the intuitive, drag-anddrop reporting and visualization features of FCS Express with the data automation and connectivity of the Luma platform. From batch analysis to quality control and assay results, everything captured in FCS Express can flow directly into Luma—ensuring traceability, consistency, and future-ready data infrastructure. Whether FCS Express is used across an entire team or alongside OMIQ in another department, all your flow cytometry data is centralized in Luma, giving your organization a single source of truth.
Spotlight: Creating a Lab-in-a-Loop for Multispecific Antibodies
Luma Antibody Discovery & Protein Therapeutics Solution
In 2024, Dotmatics announced the , the first in a series of Luma Multimodal R&D Solutions. This comprehensive solution can support each phase of the Design-Make-Test-Decide cycle in the creation of target antibodies, with an emphasis on monoclonal and multispecific antibodies. How does it work?
Biopharmaceutical companies can use the Luma Antibody Discovery & Protein Therapeutics Solution to support its pursuit of multispecific antibody therapies. The goal is to create an efficient lab-in-aloop, syncing not only its wet and dry labs, but also the workflows and decision making that support the scientists in the labs. Here is how it works:
Design: Protein Therapeutics
With BioGlyph Luma, the team dramatically increases the scale and speed of protein design evaluations— moving from manual review of hundreds of candidates to automated scoring of millions. This shift enables them to prioritize only the highest-potential designs, accelerating early discovery and reducing wasted effort on non-viable paths. Scientists gain deeper insight into candidate quality earlier in the process, improving decision-making at every downstream stage.
Make: In Silico Cloning Design & Execution
By integrating Geneious Luma into their workflow, the team streamlines the design and validation of complex antibody constructs, eliminating bottlenecks in early development. The automated flow of construct data into Luma s adaptive workflows ensures continuity between design and execution, reducing manual steps and minimizing errors. As a result, the cloning phase became faster, more accurate, and easier to scale across projects and teams.
Make: Express, Purify, & Structural Characterization
Following construct design, Luma’s adaptive workflows guide the team through expression and purification with improved consistency and fewer handoffs. Tasks that once required intensive coordination are now automated and traceable, allowing researchers to focus on interpreting results. By integrating with Protein Metrics, the team can rapidly assess protein quality and structure—enabling earlier go/no-go decisions and better-informed optimization of candidates.
Test: Functional Assay Development
With soon-to-launch Prism Luma, the team can standardize how functional assay data is captured, analyzed, and reported—eliminating inconsistencies and saving hours of manual processing. Automated normalization and data mapping ensures reliable comparisons across experiments, while built-in analysis tools will accelerate interpretation. The result is faster cycle times from testing to insight, giving scientists the confidence to advance only the most promising therapeutic candidates.
Decide: Intuitive Dashboards for Actionable Insights
Bringing it all together, the team leverages Luma’s analytics capabilities to visualize key data across protein structure, assay performance, and workflow history. Custom dashboards enable real-time decision support, helping program teams track progress, spot trends, and prioritize high-value candidates. This connected view of R&D empowers faster, more confident decisions—improving outcomes while reducing delays and duplication.
Life Science Teams See Big Results Fast
Lab Connect major multinational pharmaceutical company a top US pharmaceutical company
Since launching Dotmatics Luma in 2023, followed by , more than 2,400 connected instruments have parsed over 150 billion data records and 450 terabytes of data.
This rapidly adopted Luma, integrating 2,000 instruments in just six months —four times faster than anticipated. Similarly, rollout was so fast for the oncology R&D group at , that in one week it had five instruments and 20 users connected and onboarded. Now as a result, they’re saving multiple hours every week per each scientist. Reports are the same worldwide. Customer after customer are almost immediately able to streamline and accelerate their R&D processes.
Plus, Luma is designed to evolve with the organization, offering customizable applications and low-code app building capabilities to ensure agility, efficiency, and continuous innovation. That’s because it’s purpose-built by Dotmatics’ unmatched team specializing in science, software and operations, supporting customers in over 150 countries. With over half of Dotmatics’ world-class workforce dedicated to R&D, it deeply understands the challenges customers face.
Dotmatics software is used by more than 2 million scientists globally from BristolMyers Squibb and Merck to research universities like MIT and Oxford. In fact, many of the top 10 pharmaceutical companies helped to co-develop the platform, and are in varying phases of implementing Luma.
Luma offers a gradual, integrated path forward, allowing customers to adopt new capabilities beyond an ELN or LIMS at your own pace. With clear integration points between traditional systems and Luma, you can transition without disrupting your current workflows. This ensures continuity while unlocking new levels of efficiency and intelligence over time. And as more and more labs embrace the potential of AI-driven discovery, Luma will serve as a future-ready foundation, supporting innovation and scalability.
Future Vision: Realizing a Lab-in-a-Loop
The life science industry stands at the threshold of a once in a generation change. Scientific breakthroughs, regulatory shifts, and technological advancements are reshaping the market, forcing companies to adapt to new realities at lightning speed. We can’t predict the future, but one thing is clear: we have the opportunity to develop lifechanging treatments quicker than ever before.
In this new research landscape, AI-driven tools, automation, and multimodal discovery approaches have become fundamental to R&D. A new class of technology has emerged to meet the need—the Multimodal Scientific Intelligence Platform. A first of its kind, Luma lets researchers collaborate, reduce costs, and unlock the innovations that will shape the world.
Luma helps teams to create that earliest point of the “digital thread” linking diverse research data, decisions, and outcomes together. It marries wet and dry labs to create a transformative lab-in-a-loop that reduces costly experimentation and increases the value of data. In this lab-in-a-loop model, wet-lab results refine dry-lab models and AI and ML drive cascading optimizations, which in turn guide smarter experiments. It’s an efficiency force multiplier. Each cycle improves the next, ultimately creating a digitally transformed discovery funnel that can fuel tomorrow’s breakthroughs.
Science moves fast—Luma makes sure you move faster.
