Dotmatics x Happy Future AI – The Interview

Who is Dotmatics and what is it’s mission in the scientific informatics industry?

Dotmatics provides software to scientific R&D labs that connects their science, data, and decision-making to transform the difficult work of discovery. We have more than 2 million scientists and 10,000 customers who use our solutions today as they try to create a healthier, cleaner, safer world.

Our products are at the heart of the digital transformation of these labs and we’re helping them clear some daunting hurdles that have long plagued our industry. But 2024 is an incredibly important inflection point to our industry – a point where labs and their scientists no longer get stuck in the mire of the data deluge on their path to AI-enabled, multimodal discovery.

 Product Suite: How do Dotmatics’ products, such as Dotmatics Platform and Browser, support scientific research and discovery?

 Product Suite: How do Dotmatics’ products, such as Dotmatics Luma, Platform, and your various Scientific Applications work to support scientific research and discovery? (proposed update to the question)

Let’s start by defining a new and important term in the industry: Multimodal Discovery. At its core, multimodal discovery is the ability for scientists to pick the best therapy or combination of therapies to address a particular target.  It involves researching and testing from across different domains of science in the process of discovering new pharmaceutical, biological, or combination compounds or therapies.

Pharma and biotechs are increasingly moving from single modes of discovery toward a multimodal approach to research and discovery (R&D) for new potential targets and therapies. And the most progressive players in drug discovery are accelerating toward an AI-enabled, multimodal drug discovery future.

Dotmatics Luma is a platform built to support this future. It supports scientists as they research to choose the best therapy type or combination of therapies to address a particular target – regardless of modality.

Part of the Luma Platform is Luma Lab Connect, a powerful tool that automatically ingests and centralizes data from any file based laboratory instrument, extracts descriptive metadata and experiment results from files, and then makes data across labs and experiments available for exploration, analysis, and insights on one harmonized, low-code cloud platform, Luma.

In addition to the powerful capabilities of Luma, we have applications used by millions of scientists that have been purpose-built to support specific components of the R&D cycle such as flow cytometry, mass spectrometry data analysis, DNA cloning, genomics, and sequence analysis. The goal is to augment the science itself taking place in the lab and to empower scientists to swiftly transform data into actionable insights, enhancing decision-making processes for therapeutics or materials development.

AI Integration: How has Dotmatics incorporated AI and machine learning into its products, and what benefits has it brought to customers?

Whether the domain is pharmaceutical therapies, materials, or agriculture, Dotmatics want to support our customers who are focused on reversing the downward trend of ROI in scientific research – and we believe that AI/ML have a huge part to play in this. 

We think about that digital transformation journey toward achieving predictive and generative insights on a spectrum in three primary phases: Foundational, Transformational, and Aspirational. Many companies sit in one of the first two phases.

Most are “foundational,” they do the basics brilliantly such as simplifying the application landscape and workflows, and doing digital data capture. That means simply using software, moving off a pen and paper or spreadsheet and using software in its place. While the data might not be structured correctly, you have an understanding that software is necessary to derive efficiency. That’s where change begins.

Some companies are in the “transformational” phase; you’ve recently implemented or are in the process of implementing a platform to harmonize all their data, and perhaps are even starting to achieve analytical insights as a result. But it’s here in this early transformational phase where most companies get stuck. 

The challenge is, without a harmonized data platform, you cannot get insights at scale from that proprietary data. Much of the industry struggles with complex, massive, yet isolated data points from applications and instruments, and bringing all of these data points together between applications into a workflow will likely consume much of the next few years. And that’s a big problem. Because, beyond your people, we know our customers’ number one asset is your proprietary data. These data come from years of experience and knowledge gained in the discovery process, and it’s what separates a company from their competition. 

Ultimately everyone is moving towards the “aspirational” phase—first achieving lab automation, then starting to utilize AI to gain new insights in research, and ultimately, performing in-silico methods, and simulations. It’s this phase that we so commonly hear our customers say that they need help from the right technology partner to get there, to address those data silos, interoperability, and workflow problems.

Through our Dotmatics Luma Platform in particular, Dotmatics is helping its customers into this aspirational phase. Luma Lab Connect aggregates, processes, models, and analyzes data from any laboratory instrument or other data source from one platform. It lets labs create and execute queries using natural language processing and generative AI to provide an unprecedented ability to easily analyze complex relationships. 

 AI-Powered Research: How do you see AI changing the scientific research landscape, and how is Dotmatics positioned to support this shift?

AI is fundamentally transforming the scientific research landscape by enhancing the speed and accuracy of data analysis, enabling more complex and nuanced experiments, and facilitating breakthroughs at an unprecedented pace. It’s taking the traditional drug discovery funnel and shortening it while vastly widening the initial possibility of compounds and therapies considered. AI can automate routine tasks, allowing for the modeling of complex biological systems, and can predict outcomes from vast datasets that are beyond human capacity to analyze efficiently. This leads to more precise experiments, innovative drug development, and smarter, faster scientific discoveries.

AI is sort of like a powerful flashlight that can illuminate hidden patterns and insights that exist in vast amounts of data – allowing us to see and understand things that were previously too dark to see. Now we enable customers to explore areas that haven’t been considered. 

We think that Dotmatics is well-positioned to support this shift through our comprehensive suite of advanced software solutions that support an AI-enabled, multimodal world of discovery.

AI Ethics: How do you ensure responsible AI development and deployment at Dotmatics, particularly in the scientific research space?

At Dotmatics, responsible AI development and deployment are ensured through adherence to strict ethical guidelines, promoting transparency, fairness, and accountability. The company emphasizes transparency by clearly documenting AI

processes, enhancing understanding and trust among users. To combat bias, AI algorithms undergo continual testing with diverse datasets, ensuring fairness across different populations. Accountability is maintained by robust testing and stakeholder engagement before AI deployment, assessing ethical implications and potential risks. Additionally, Dotmatics upholds data security and privacy by implementing advanced protection measures and complying with international data regulations, ensuring that its AI-enhanced tools not only advance scientific research but also align with the highest ethical standards.

AI Trends: What AI trends do you see shaping the scientific research and informatics industry in the next 5-10 years?

I see there being a few areas where AI will be deeply involved in scientific research during the coming decade. Let’s start with the world of what we might call biomedical and basic sciences and how AI will play a part. These are among the applications of AI that get the most buzz when it comes to life sciences. 

• Genomic Analysis & Personalized Medicine: Scientists can analyze large-scale genomic data to identify genetic variations associated with diseases, predict patient outcomes, and develop personalized treatment plans. 
• Pharmacogenomics: Integrate genomic and clinical data to predict individual responses to drugs based on genetic variations, enabling personalized dosing and treatment strategies.
• Disease Diagnosis & Biomarker Discovery: Analyze clinical and -omics data to improve disease diagnosis, identify disease biomarkers, and develop diagnostic tests for early disease detection. 

Beyond those, let’s consider the application of AI to a few key areas of the drug discovery & development process. These are processes that are happening in the R&D labs which could each help to reduce the significant costs and time associated with bringing therapies to market.

• Drug Target Identification: Analyze biological data to identify novel drug targets, including proteins, genes, and pathways implicated in disease processes
• Protein Structure Prediction & Design: Predict protein structures, interactions, and functions, aiding in protein engineering, drug target, identification, and rational drug design.
• Drug Discovery & Development: The overall process can be improved by identifying potential drug candidates, predicting their efficacy and safety profiles, and optimizing the drug design process.