A major milestone for American semiconductor manufacturing. Today, SandboxAQ announced a definitive agreement with the U.S. Department of Commerce for a $500 million CHIPS award to help tackle one of the most urgent challenges in American manufacturing: securing the critical materials and chemistries that underpin semiconductor production. Using Large Quantitative Models (LQMs), SandboxAQ will advance innovation across four material categories essential to the future of the industry: • PFAS-free process chemicals • Advanced catalysts • Rare earth-free magnets • Next-generation battery systems Across all four categories, the opportunity is the same: move critical materials from discovery to industrial deployment faster than ever before and strengthen the foundation of American semiconductor manufacturing. Read the full release here: sandboxaq.com/post/sandboxaq…

SandboxAQ Essential Concepts – MKG Explained Your heart’s electrical activity creates more than an electrical signal, it also produces a faint magnetic field. Magnetocardiography (MCG) uses highly sensitive magnetic sensors to detect that signal outside the chest and translate it into a map of the heart’s magnetic field. Because the signal is so faint, high-sensitivity sensing and signal processing help separate it from environmental noise. The result is a different window into the heart’s electrical activity, using magnetic fields to capture information about cardiac activity. Learn more about how MCG works with our infographic below 👇

SandboxAQ Essential Concepts – Quantum Sensors Are Already Everywhere What if quantum sensing isn’t a technology of the future, but one you’ve already been relying on for decades? Quantum sensors are already behind technologies we use every day. MRI machines have harnessed quantum properties since the 1970s, using the behavior of atoms in the body to create detailed images. Atomic clocks, developed decades earlier, define the official SI second and keep precise time aboard GPS satellites. What’s changing isn’t the arrival of quantum sensing. It’s where the technology can go next. Advances are enabling a new generation of quantum sensors designed to be smaller, more rugged, and sensitive enough to operate far beyond hospitals and satellites, opening the door to entirely new applications. Learn more about the quantum sensors already all around us, and what’s coming next, with our infographic below 👇

Some of the most interesting targets in a portfolio have never been crystallized. AQPotency ranks protein-ligand pairs by predicted binding strength (pIC50) from two inputs: a target's UniProt ID and candidate SMILES - no experimentally resolved 3D structure required. That opens programs that structure-based methods cannot reach, including membrane targets and proteins that have never been crystallized. AQPotency also scores roughly 2,000 protein-ligand pairs in about 15 seconds, at roughly one dollar per thousand on Claude. On October 7, Nihit Pokhrel, PhD and Phyo Phyo Zin, PhD walk through three workflows: single-target virtual screening, reverse screening, and selectivity and off-target analysis. Event Details: 🔸 Wednesday, October 7 🔸 11 AM–12 PM PT 🔸 Live Q&A at the end Save your seat here: sandboxaq.com/webinars/aqpot…

What do spoof-proof aircraft navigation, heart attack detection, a $500M CHIPS Act grant, and a trash espresso machine have in common? They’re all part of Ben Yaffe’s delightfully nebulous role at SandboxAQ. In the latest episode of the fAQ podcast, Ben sits down with Tai-Danae Bradley to talk about the projects, career detours, and bias for action that have shaped his work across SandboxAQ. From AQNav and AQMed to AI simulation, search and rescue, and learning from failure, this is a conversation about what happens when you follow the interesting problems. 🎙️ Watch the full episode on YouTube: piped.video/watch?v=-MwRgAcb… CardiAQ is an investigational device. Limited by Federal (or United States) law to investigational use.

More AI agents. More code. More review. Less shared understanding of the codebase. Timo Bozsolik-Torres, Head of Engineering, spoke with Dev Interrupted about a new bottleneck emerging as AI changes how engineering teams work. As Timo puts it, the answer probably isn’t to “review harder.” It’s figuring out how agents can help with review and comprehension too, not just writing more code. Read more: devinterrupted.substack.com/… Explore Switch: switchagents.ai/

SandboxAQ Essential Concepts – Your Heart Is Producing a Magnetic Field Right Now Your heart generates electrical activity while also creating a magnetic field. Every heartbeat is driven by electrical activity in the heart. That activity produces a magnetic field that extends beyond the chest, where it can be measured using magnetocardiography (MCG). MCG maps these heart-generated magnetic signals, providing a complementary view of cardiac electrophysiology and demonstrating how magnetic sensing can reveal information beyond what we can see from the heart’s electrical signals alone. Learn more about the magnetic field your heart produces with our infographic below 👇

An AI model that ranks well on a benchmark and an AI model that changes a project decision are not the same thing. Closing that gap is the subject of this week's panel at Discovery on Target. Victor Sebastian Perez, PhD, Head of Computational Drug Design, EMEA from SandboxAQ, joins the panel discussion Bridging AI/ML Tools and Real-World Drug Discovery — Thursday 1 October, 9:35 AM ET. With all the recent drug discovery advancements, we can now generate molecules, structures, antibodies, and hypotheses at unprecedented scale. Victor Sebastian Perez will discuss the opportunity now to build intelligent, closed-loop discovery systems that combine models with biological context, experimental data, and active learning to decide what to make, test, and pursue next. Attending Cambridge Healthtech Institute’s Innovative Discovery Technologies conference? Find Víctor on Thursday morning.

本日より、「高機能素材Week」が幕張メッセにて開幕します。 当社ブースは 第4ホール・ブース番号25-19です。 9月30日~10月2日の会期中はVice President of Business Development, Chemical Simulation の Deren Koseoglu がブー スに常駐し、触媒、電池、PFAS代替材料、合金にわ たるMaterial Discoveryについてご説明します。 まずはAQCatをご覧ください。スピン分極を考慮した 当社の触媒モデルで、npi Computational Materialsに 掲載されています。 DFTに迫る精度を最大20,000倍の 速度で実現し、スピン分極を明示的に取り扱います。 これは鉄・ニッケル・コバルトの表面を扱ううえで重 要な点です。詳細はこちら: nature.com/articles/s41524-0… また、Business Development - Japanの永井文策が10月2日 (金) 15:00~15:45に「 Al + Physics for the Next Generation of Materials」 と題した講演を行います。会場: Next Tech STAGEとなります。 ぜひブースにお立ち寄りください。一人でも多くの方と材料探索について お話しできることを楽しみにしています。 Highly-functional Material Week opens today. Find us at Booth 25-19, Hall 4. Deren Koseoglu is on the stand September 30 – October 2 to talk through Material Discovery across catalysts, batteries, PFAS alternatives, and alloys. A good place to start: AQCat, our spin-aware catalyst model, published in npj Computational Materials. It reaches accuracy approaching DFT up to 20,000 times faster, and it treats spin polarization explicitly — which matters for the iron, nickel, and cobalt surfaces. Read more here nature.com/articles/s41524-0… Bun Nagai will also be giving a talk on Friday October 2 at 15:00 - 15:45 around AI + Physics for the Next Generation of Materials. Location: Next Tech STAGE. Come find us and talk about material discovery!

Earlier today we shared news of our Nature Communications paper with Baylor College of Medicine. Chemical & Engineering News has now covered the work. Molecular glues pull two proteins together and prompt the cell to destroy a target it would otherwise leave alone. That mechanism opens up targets the field has long considered undruggable, including VAV1. The C&EN piece focuses on how we found these molecules. Baylor screened a small library of chemical fragments using high throughput proteomics and identified two early hits. SandboxAQ then applied AQFEP, our physics based free energy platform, to model how those compounds engage VAV1 and to show chemists where to improve them. Both teams told C&EN that starting with fragments makes molecular glue discovery more affordable and more accessible than conventional approaches. Congratulations to Rae (Rui) Qi, Ly Le, and Andrea Bortolato, and to our collaborators at Baylor. Read the C&EN article: cen.acs.org/pharmaceuticals/… Read the paper: lnkd.in/ekxSiKV7 Baylor's press release: lnkd.in/efsTe_dw

SandboxAQ Essential Concepts – What Makes a Sensor Quantum? What makes a quantum sensor different from a classical sensor? Both can measure the same things: magnetic fields, light, temperature, pressure, and more. The difference is in how they measure them. Quantum sensors harness the laws of quantum mechanics to detect extremely faint signals that traditional sensors may not be able to perceive. And because their measurements can be tied to fundamental constants of nature, they can remain accurate over time without the drift and frequent recalibration associated with many classical sensors. That combination of sensitivity, stability, and precision can open new possibilities in areas where even tiny changes matter. Learn more about what makes a sensor quantum with our infographic below 👇

Congratulations to our SandboxAQ colleagues Rae Rui Qi, Ly Le, and Andrea Bortolato on an exciting milestone in our ongoing collaboration with Baylor College of Medicine — a new paper published in Nature Communications! Read the paper here: nature.com/articles/s41467-0… VAV1 is a key target in hematological malignancies and autoimmune diseases and has long been considered challenging to drug. This collaboration is a powerful example of how experimental biology, AI, and physics-based approaches can come together to tackle that challenge. Baylor’s high-throughput proteomics and experimental validation, combined with AI-based ternary-complex structure modeling and FEP, helped identify and characterize molecular glue degraders for VAV1. This integrated approach identified a non-canonical RT-loop degron and enabled prospective ranking of molecular glue analogs based on predicted ternary-complex cooperativity. Our SandboxAQ team contributed AQFEP, our physics-based free-energy platform, to characterize and rank molecular glue candidates, with predictions consistent with experimental degradation results. We’re proud to see computational science and experimental biology come together in this collaboration, and we look forward to continuing our work with Baylor to advance molecular glue discovery. See Baylor’s Press Release at: bcm.edu/news/ai-structure-pr…

SandboxAQ Essential Concepts – SAIR: The Structural Context Behind Better Drug Discovery Models What if AI models had access to both measured drug potency and the 3D structure of the protein-ligand interaction? That’s the problem SAIR is built to address. SAIR brings together 5.24 million co-folded 3D protein-ligand structures with 1M+ experimentally measured, IC50-tagged protein-ligand pairs, giving AI models structural context that conventional potency datasets often lack. Here’s how it works: → Start with measured potency data from ChEMBL and BindingDB → Use AI to predict 3D protein-ligand structures → Generate multiple structures and computationally assess them → Release the structures alongside experimental potency, physical-validity checks, and model confidence The result is an open dataset designed to help models learn not just whether a molecule binds, but the structural context behind how it interacts with its target. SAIR is freely available for non-commercial use, with commercial access also available after a short form. Learn more with our infographic below and explore the dataset: sandboxaq.com/sair

What does it take to turn advanced sensing of the heart's magnetic field into a medical device and build the kind of team that can take it there? On the latest fAQ Podcast, Tai-Danae Bradley sits down with Kit Yee Au-Yeung, General Manager of AQ Med at SandboxAQ, to talk about building an AI-powered magnetocardiography device that is designed to capture the heart’s magnetic signals at the bedside, without the need for a cumbersome metal shield. Kit also gets into the less technical, and equally important, side of deep tech: leading through uncertainty, bringing quantum physicists into regulatory work, knowing when past playbooks no longer apply, and embracing the “tiger beetle” mindset. Tune in for a thoughtful conversation on building technology—and teams—at the frontier: piped.video/watch?v=_qHXuToM…

Heading to Highly-functional Material Week, MS&T26, or The Advanced Materials Show? Let’s talk about accelerating your materials R&D—from catalyst screening to alloys, batteries, and PFAS alternatives. 🚀 We'll be on hand to discuss the full ChemSim platform, including AQCat, our spin-aware catalyst model published in npj Computational Materials (nature.com/articles/s41524-0…). It delivers near-DFT accuracy up to 20,000x faster and uniquely accounts for spin polarization in key metals like iron, nickel, and cobalt. Catch our team on the floor: 🗓️ Sep. 30 - Oct. 2 | Highly-functional Material Week: Meet Deren Koseoglu to discuss the full ChemSim platform, including catalysts, batteries, PFAS alternatives, and alloys. 🗓️ October 4 - 7 (MS&T26) & October 6 - 7 (The Advanced Materials Show): Join Scott Healey, Tanner Kirk, and Sydnee King as we dive deep into alloys. See you there!

SandboxAQ Essential Concepts – Binding Affinity: The Molecular “Handshake” Behind Drug Discovery How do scientists know which drug candidates are worth making and testing? One important clue is binding affinity — a measure of how strongly a molecule binds to its biological target. A tighter molecular fit can mean a more stable complex, helping researchers identify the candidates most worth pursuing. But affinity is more than a number. It can help teams prioritize promising molecules, optimize designs, and de-risk candidates before they reach the lab. SandboxAQ combines AI with physics-based methods through its Large Quantitative Models (LQMs) to predict binding affinity, rank candidates, and help drug discovery teams search chemical space faster. The goal? Fewer guesses. Smarter experiments. Faster paths to promising drug candidates. Learn more with our infographic below 👇

SandboxAQ Essential Concepts – Drug Discovery: Small Molecules and Biologics Small molecules are exactly what they sound like: relatively small, chemically synthesized compounds. Their size allows them to cross cell membranes, making them especially useful for reaching targets inside cells. Many familiar medicines, from aspirin to statins and antibiotics, fall into this category. Biologics are much larger and more complex. Produced by living cells, they include antibodies, insulin, mRNA vaccines, and nanobodies. While their size generally keeps them outside cells, biologics can target cell-surface and extracellular proteins with high precision. Neither approach is inherently better. The biology of the target determines which modality makes the most sense, and some of the most difficult targets require new ways of designing and evaluating candidates. SandboxAQ’s Large Quantitative Models (LQMs) use AI grounded in physics to help design, screen, rank, and optimize candidates across both small molecules and biologics. The goal: expand what’s possible in drug discovery and find promising candidates faster. Small molecules and biologics are complementary tools. Choosing the right one is only the beginning. Learn more below.

SandboxAQ Essential Concepts – How Some Animals Sense Earth's Magnetic Field How do migratory animals navigate the globe without GPS? Part of the answer includes magnetoreception, a natural ability to read Earth’s magnetic signatures. Understanding how nature solves navigation challenges can also inspire new approaches to technology. It’s the same core idea behind SandboxAQ’s AQNav: using advanced sensing and physics to understand position and direction in environments where traditional navigation systems may fall short. Explore further in the infographic below!

We published a new blog post on the science behind AQCat, the model that treats spin polarization explicitly for iron, nickel, and cobalt at up to 20,000 times DFT speed. Those metals catalyze ammonia synthesis, which makes the world's fertilizer, and Fischer-Tropsch, which converts syngas into liquid fuels and chemical feedstocks. Both run at enormous industrial scale. Large public catalyst datasets tend to skip spin polarization because it costs far more compute, which leaves spin-unpolarized models wrong exactly where this chemistry lives. Omar Allam, Brook Wander, Sungyeon Kim, Aayush R. Singh and the catalysis team trained AQCat from scratch on the 13.5 million single-point DFT calculations in AQCat25, jointly with 20 million OC20 examples. That joint training gives the model its magnetic reach while it holds accuracy parity on general chemical space. npj Computational Materials published the peer-reviewed work, and we released the AQCat25 dataset, model checkpoints, and training code openly for non-commercial use. Read the full blog: sandboxaq.com/post/how-aqcat…

If you're excited about working on the frontier of positional intelligence and solving complex real-world problems. Come by booth 416 during AFA to meet some of our team! CTA: piped.video/-GxPYx3ssNQ #AFANational #positionalintelligence #quantumnavigation #altpnt

SandboxAQ Essential Concepts – Navigating Using Earth’s Crustal Magnetic Field Magnetic minerals in Earth’s crust create local variations in the planet’s magnetic field. These variations form patterns that can be mapped and used as a passive reference for positioning. The process is straightforward in principle: map the magnetic patterns, measure the field, and match the measurement to the map, but in practice, the magnetic map patterns can be hidden underneath an overwhelming amount of noise from the aircraft itself! That's where AQNav's AI-enhanced algorithms come in. By leveraging LQMs, we are able to separate the tiny signal from the noise. A magnetometer captures the magnetic field while navigation software compares the observed pattern against a magnetic anomaly map. Those matches can provide position updates and help bound drift in inertial navigation systems. This approach doesn’t replace existing navigation technologies. Instead, it adds another independent reference, using the Earth itself as part of the navigation system. Explore the infographic below to see how Earth’s crustal magnetic field can become a tool for navigation. 👇