SigmaticSciences @SigmaticScience

Our Head of GTM, David Cooney, is heading to Decoding Bio's AI x Bio Summit in New York this week on July 23 – joining the AI-biology community to discuss what comes next in the industry. AI is everywhere in life sciences R&D right now, but coordination is still a problem. We're looking forward to sharing how SigmaticOS connects the tools, data, agents, and workflows scattered across R&D operations into one platform, so scientists spend less time managing complexity, and more time moving discovery from copilot to autopilot. If you're attending, let's connect. Dave would love to share what we've been building and hear what you're working on. #DecodingBio #AIxSummit #LifeSciences #DrugDiscovery #RandD

Drug discovery is not a single step but a cascade of workflows and vital trial-and-error loops, from screening thousands of compounds to iterative optimisation of pharmacokinetics and safety. But in most labs, the loop is slow and cumbersome. A question leads to an experiment. An experiment leads to analysis. And that analysis should immediately inform what comes next. But this doesn’t happen. Instead of building momentum, scientific teams hit fragmentation with: -Models operating in isolation -Data scattered across disconnected tools -Wet lab and in silico running in parallel, rarely in sync Closing that loop isn't about doing more. We need systems designed to support the full cycle — end to end — so discovery can move at the speed the science deserves. How connected is your discovery loop right now — and where does momentum most often break down in your lab? 👇 #LifeSciences #AI #DrugDiscovery #Biotech #Innovation

Big news: Sigmatic Sciences is deepening its collaboration with @nvidia, integrating NVIDIA BioNeMo into SigmaticOS, building on our existing use of NVIDIA NIM microservices to bring even more biological intelligence to our agents. Read the announcement here: sigmaticsciences.com/press-release/… When AI agents are fluent in biology, scientists can spend less time managing isolated tools and more time on discovery. As one of the first platforms to leverage these new BioNeMo capabilities, we’re giving research teams: -Accelerated agentic workflows – so scientists move faster from hypothesis to experiment -Domain-specific biological AI – so agents understand your science, not just process it -Reduced orchestration costs – so teams focus on R&D outcomes, not data wrangling -New task capabilities – structure prediction, molecular design, genomics, in one place -Faster in silico hypothesis testing – so complex theses can be tested with fewer handoffs This means closing the loop between computation and the bench, enabling research teams to move faster with confidence with fully autonomous, traceable scientific workflows. As our Founder and CEO, Kevin Cramer, put it: "Together, we’re creating a new model for discovery where AI can reason across data, use specialized scientific tools, and interact directly with the laboratory as part of a single traceable workflow.” We'll be showcasing SigmaticOS and the benefits of the collaboration at the BIO International Convention this week – come find us. Many thanks to the team at NVIDIA for their continued support. #SigmaticOS #LifeSciences #AI #DrugDiscovery #NVIDIA #BioNeMo #Biotech #LabInTheLoop

AI is rapidly becoming one of the most important drivers of innovation in biotech, creating new opportunities to accelerate discovery, improve decision-making, and transform R&D. That's why we're looking forward to next week's BIO International Convention in San Diego, where our Head of Science Office, Dr Rob Brown, and Head of GTM, Dave Cooney, will be joining leaders, researchers, and innovators at the AI Summit to explore how AI is being meaningfully implemented across the life sciences. They'll be sharing how SigmaticOS connects tools, data, agents, and scientific workflows in a unified platform, helping scientists learn faster, streamline complex processes and removing operational burden, to move from copilot to autopilot across research and development. If you’re attending, let’s connect. Rob and Dave would love to share what we’ve been building and hear what you’re working on. Drop a comment, send us a DM. #BIO2026 #DrugDiscovery #LifeSciences #Biotech #AI #Innovation

Recent reports suggest only a minority of R&D organizations are fully able to leverage AI in their discovery programs, even as use accelerates across pharma, why is that? 📊 For a space with some of the richest, most varied datasets ever built, with decades of structured and unstructured research data, that number should be much higher. It’s clear it’s not a lack of ambition or tools. The root of the issue is operational burden coupled with the myriad of complex tools scientists are being asked to use. R&D teams are shrinking, yet are expected to progress the same number of programs. What scientists need is a platform that can orchestrate layers of complexity and remove the operational burden faced by scientists in the modern R&D organization. We require specialized agents that work in concert in a single automated workflow – enabling drug discovery to go from copilot to autopilot. What's the biggest barrier your organization has hit when trying to put AI to real use in R&D — and how much of it comes down to the data itself versus the systems around it? 👇 #LifeSciences #AI #DrugDiscovery #Biotech #Innovation

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Sigmatic Sciences has been selected as a Rising Star presenter at the DIA 2026 Global Annual Meeting tomorrow in Philadelphia. As AI continues to reshape drug discovery, one of the biggest challenges facing R&D teams is turning scientific knowledge into action across increasingly complex workflows, data sources, and systems. Our Head of GTM Dave Cooney will be joining fellow innovators during the Biotech session at the Innovation Theater at 9.15-10.00am tomorrow to share how we're thinking about the future of AI-enabled discovery and the infrastructure needed to support it. If you're attending DIA, we'd love to connect and discuss the opportunities and challenges shaping the next generation of drug discovery. Drop us a message or reach out to arrange a meeting. #DIA2026 #DrugDiscovery #LifeSciences #Biotech #AI #Innovation

The growth of AI in R&D is clear to see, with roughly 70–80% of pharma and biotech organizations now using AI in at least one part of drug discovery. However, when you dig down, less than half have fully embedded it into core discovery workflows. A lot of R&D teams are experimenting with AI, but very few have fundamentally changed how their R&D operation functions, despite the availability of cutting-edge tools and improved models. So why is that? Because the issues and challenges of drug development haven't changed: -Experiments still happen in isolation 🔬 -Results still need to be pulled together manually 📊 -Learnings don't carry cleanly into the next cycle 🔄 Fully realising the potential of AI in R&D isn't achieved through minor experimentation. It only works when it underpins how the system operates — when each experiment builds on the last, and learning compounds over time rather than having to be rebuilt after every experiment. ⚙️ That's what being AI-ready actually means. Not access to better models. But an environment where data, workflows, and experimentation are connected enough for that learning to carry forward. So where is your organization on that journey — and is AI genuinely changing how your R&D compounds knowledge, or is it still largely a collection of standalone use cases? 👇 #AI #LifeSciences #Automation #DrugDiscovery #Biotech #Innovation

Life sciences R&D isn’t short on technology; there are hundreds of powerful tools, each solving a specific part of the process. AlphaFold has revolutionized structural prediction, and tools like Schrodinger Suite, MOE and GROMACS provide the industry backbone to molecular modelling and simulation. But across mid-size biotechs to pharma, that landscape has become deeply fragmented. 🔗 Teams are now trying to operationalise 800+ tools, only to get pulled into constant “data wrangling” — cleaning, transferring, and formatting data just to make it usable. It’s become clear that these tools weren’t designed to integrate at scale: -Data gets stuck between steps -Workflows break across systems -Teams spend time manually connecting the pieces The solution isn't about more technology, but rethinking how that technology works together. Is your organisation still evaluating tools in isolation — or starting to think about how they connect? And what would it take to make the latter the default? 👇 #AI #LifeSciences #Automation #DrugDiscovery #DataStrategy

Reference: onlinelibrary.wiley.com/doi/10.1111/po…

Across 1,274 early-stage discovery projects, researchers found that scientific teams were spending far more time on decision-making, project management, and evaluation instead of experimentation. ⏱️ With today’s focus on speeding up the sluggish drug discovery process, we should be looking at the slowdown that comes after experiments that slows down progress towards more time-consuming strategic decision-making, with: -Results scattered across systems -Data manually pulled together -Findings interpreted against past work before any decision can be made But when data, models, and workflows are properly connected, each experiment feeds straight into the next. This means results are immediately usable, context is already there and the next step is clear. The value of experiments isn't just in running them — it's how quickly findings translate into better decisions, and ultimately, better medicines. How much of your team's time goes on the work between experiments — and what would change if that shrunk? 👇 #DrugDiscovery #DrugDevelopment #LifeSciences #Biotech #Innovation

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In drug discovery, key decisions still take many months because the initial discovery cycle is hampered by a staggering timeline of 3-6 years, which is fraught with slow, data driven workflows⏱️ This isn’t because the process is inherently slow - to facilitate faster discovery decisions it will depend on the whole system working together across the full value chain, with:  -Connected data ecosystems supporting seamless enterprise-wide operations  -Models connected end to end to accelerate integration at scale  -Workflows stitched together before any decision can be made  We no longer need to imagine dramatically compressed timeline while maintaining the same scientific rigor, with a system that iterates continuously - testing more hypotheses, learning faster, refining direction in real time.  A misunderstanding in modern R&D is that it’s just about moving faster. In reality, it's about how quickly your scientific teams can learn what works and what doesn’t - and act on it with confidence.  Where in your R&D process does time get lost most - and how much of that is a workflow problem rather than a science one? 👇 #LifeSciences #AI #Automation #DrugDiscovery #HumanInTheLoop

Scientists spend an estimated 30% of their time managing transitions between disparate systems. Some estimates put this as high as 50% when factoring in data discovery, data management, and related admin tasks. But, scientists don't go into R&D to manage workflows, they enter R&D to discover new drugs that save patients’ lives. But for many, that's not where the time goes. Instead, it goes on: •Integrating tools and models that weren't built to work together  •Wrestling with fragmented, heterogeneous data just to get started  •Manually connecting tools and processes that should already be connected How much time do you actually spend on what you joined R&D to do? 👇

Current evidence suggests AI integration in R&D remains largely in copilot, with the adoption of AI in R&D organizations estimated around only 30%. 🛩️AI can support experimental design, accelerate workflows, and help scientists progress more efficiently to subsequent stages of discovery. However, R&D processes often remain fragmented, requiring human input and oversight at each of these steps: -Connecting the tools ⚙️ -Designing workflows 📐 -And managing how systems operate as a whole 🔗 The opportunity lies in transitioning from isolated tasks to integrated, end-to-end workflows that can run autonomously, learn, and improve with every cycle — without needing to be a data scientist, and without manually stitching systems together. Are you a life science company facing growing pressures that demand new approaches to growth, productivity, needing to switch from copilot to autopilot? Tell us what's standing between where you are now and truly automated R&D workflows? 👇 #LifeSciences #AI #DrugDiscovery #DataStrategy

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In silico tools began emerging in the 1950s and 1960s with the advent of QSAR and later took off in the 1980s and 1990s with the incredible advances in molecular graphics and computing power.   Despite their proven capability, in silico tools remain disconnected from daily R&D workflows—scientists, either through lack of expertise or resources, hesitate to apply them ubiquitously across pharma teams. This stems from risk aversion, where scientists stick to familiar methods and hesitate to embrace new ways of doing things.  Therefore, instead of becoming an integral and central part of the discovery process, they remain separated.  This is the fundamental barrier that is holding back the real promise of AI in drug discovery. The capabilities of the tools themselves are without question, but the ability to integrate them into the way that actual labs run still has some way to go.  Which raises the question: how are you actually using in silico tools today — and where are they falling short? 👇 #LifeSciences #DrugDiscovery #AgenticAI #Innovation #Biopharma

The gulf between small and mid-sized biopharma companies is staggering. Between 2025 and 2030, pharma investment in AI is expected to grow from $4 billion to $25 billion, an astonishing 600% increase. Yet despite this, smaller teams are still expected to: ● Execute projects faster ● Validate targets sooner ● Identify candidates with less resource Surprisingly, in spite of the growing chasm between pharma and biotech, the challenge for most companies isn't access to AI, but the ability to deploy it. Many teams encounter bottlenecks in how AI is integrated, with fragmented tools, siloed data, and disconnected workflows. Agents aren't able to achieve their objectives without access to the right data and scientific context. Pharma and biotech, whatever size, that connect their data, tools, and workflows into systems that actually learn will seize a meaningful advantage over their competitors big and small. The ones that don't will fall further behind — regardless of how many AI tools they've subscribed to. So instead of simply asking "Are you using AI?", you should be focusing on how deeply AI is connected within your R&D operations. How are you making more with less to compete with pharma? Let us know in the comments below 👇 hashtag#LifeSciences hashtag#AI hashtag#DrugDiscovery hashtag#Automation hashtag#Innovation

What a great two days at the Pistoia Alliance 2026 Spring Conference, where our Director of AI Solutions, Marcos Camara, delivered a standout presentation exploring the evolving demands on AI scientists orchestrating multi-agent workflows, who are increasingly working across diverse and complex datasets, and how SigmaticOS is designed to meet these challenges. Conversations with research leaders emphasized that well-defined semantics and robust ontologies are becoming central to effectively coordinating complex, data-rich systems. As agentic systems expand into broader scientific domains, the importance of rigorous data provenance frameworks also grows. At Sigmatic Sciences, we are excited to continue advancing the power of AI agents. We equip scientists to accelerate research and development in biopharmaceutical organizations, explore hypotheses more efficiently, and prioritize the most promising drug candidates for further development. Thank you to Pistoia and our partner @ElsevierConnect for joining us on stage and contributing to such an important conversation on the future of AI-native R&D. #LifeSciences #AI #DrugDiscovery #RandD #Innovation

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Paulo has joined our AI Solutions team at Sigmatic Sciences with four years of experience in AI and Machine Learning, backed by a strong foundation in Electrical and Electronic Engineering. Most recently, he's been consulting at @Shell, where he built agentic AI solutions for equipment diagnostics and predictive maintenance — exactly the kind of intelligent, end-to-end automation that defines what Labs of the Future will look like. At Sigmatic, Paulo will be expanding our Life Sciences agent catalogue and strengthening our AI/ML capabilities in SigmaticOS. We couldn't be more excited to have him on board. Outside of work, you'll find him hiking, running or cycling. Welcome to the team, Paulo — great things ahead! #NewTeamMember #WelcomePaulo #AIandML #LifeSciences #SigmaticSciences #LabsOfTheFuture

We’re heading to the Amazon Web Services (@awscloud) Life Sciences Symposium 2026 in New York on April 15! We’re excited to be part of the conversation and to connect with others, including our partner #AWS, to discuss how SigmaticOS is pushing the boundaries of innovation in the space and streamlining complex workflows across research and development. If you’re attending, let’s connect. We’d love to share what we’ve been building and hear what you’re working on. Drop a comment, send us a DM, or book time with us directly here: shorturl.at/ScPMc

Most breakthrough ideas never make it past the lab. Not because the science isn’t there, but because the operational bottleneck of turning a hypothesis into a rigorous research plan — and executing it — slows progress before it begins. The result? Promising hypotheses stalled. Resources tied up in manual processes. Teams defaulting to safer, less transformative targets. What if you could change that? → sigmaticsciences.com/request-demo/?… SigmaticOS unifies computation, automation, and human reasoning into a single lab-in-the-loop system. It understands your scientific objectives, integrates your tools, datasets, and models, and helps convert questions into actionable research workflows. The impact is measurable: ✓ More hypotheses explored per year ✓ Faster time from hypothesis to result ✓ Lower cost per hypothesis ✓ Higher quality candidate selection ✓ Reduced early-stage attrition By expanding the discovery funnel computationally, you can prioritize in silico and focus wet lab resources only on the most promising candidates. Stop leaving breakthroughs on the table. #DrugDiscovery #AIinPharma #Innovation #RnD

Scientific discovery has always been a cycle: hypothesis, experiment, analysis, iteration. So why do most organizations still run it in fragments? → sigmaticsciences.com/?utm_source=Li… Models operate in isolation. Labs run in parallel. Data is scattered across tools that don’t talk to each other. It’s not a technology problem—it’s a connectivity problem. Systems don’t learn together. We asked a simple question: what if they did? What if computation, automation, and human reasoning formed one continuous loop—where experiments inform algorithms in real time, models evolve with every iteration, and insights flow seamlessly between virtual and physical labs? That’s SigmaticOS: an agentic operating system for life sciences R&D, unifying the digital, physical, and human sides of drug discovery. Because innovation doesn’t come from doing more experiments. It comes from making every experiment count. #AI #LifeSciences #DrugDiscovery #Automation #Innovation