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Jack is the CEO of SandboxAQ, an enterprise SaaS company delivering AI and quantum tech…
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Jack Hidary shared this#Agents working with humans is the new way to work. Our Switch agentic platform is open source and free — and works with Slack, Teams, Discord and other collab tools. We are #1 on Product Hunt today! https://lnkd.in/eu5rbxheSwitch: Bring any AI agent into Slack, Teams & Discord | Product HuntSwitch: Bring any AI agent into Slack, Teams & Discord | Product Hunt
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Jack Hidary shared thisvery proud of the team with this new advanceJack Hidary shared thisScaffold hops are where relative binding free energy (RBFE) methods most often fail. They are also where many of the most strategically important hit-to-lead decisions are made. SandboxAQ's NEX (Nonequilibrium Chimeric Switching) method achieved a Pearson correlation of r = 0.71 against experimental values on a 45-transformation BACE1 scaffold-hopping benchmark, with no manual protocol customization, no hand-tuned corrections, and no manual restraint selection. Most predictions landed within 1 kcal/mol of experimental values, the standard threshold for reliable RBFE signal. The July 28th webinar with Mary Pitman, Lee Huntington, Dominic Rufa, and Amogh Sood will cover benchmark results, fault tolerance, and what NEX means for teams running chemically diverse hit-to-lead campaigns. Register here: https://lnkd.in/ek8G2n83
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Jack Hidary shared thisHappy July 4th to all! On this 250th anniversary of our Nation, let’s share our appreciation for our freedom and liberty. Let’s share our commitment to defend this freedom and liberty and uphold the enduring democracy that is the USA. 🎆 🇺🇸
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Jack Hidary shared thisToday’s White House Executive Order 14409 marks a new era in America’s cyber defense posture: The transition to post-quantum cryptography is now an imperative. The standards exist. The challenge now is implementation. Governments, financial institutions, energy providers, telecommunications networks, and other critical infrastructure operators must inventory and upgrade cryptographic systems before quantum computers are capable of breaking today’s encryption. At SandboxAQ we are already working with federal agencies to implement the NIST standards and now with this EO we can accelerate the work. https://lnkd.in/dz7bq7EhQ-Day moved closer: PQC migration timelines just shifted leftQ-Day moved closer: PQC migration timelines just shifted left
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Jack Hidary shared thislet’s go! time for great impactJack Hidary shared thisWhat a historic day for SandboxAQ and the entire semiconductor materials ecosystem! 🎊 U.S. Department of Commerce CHIPS R&D Office has awarded $500million to SandboxAQ to discover and develop semiconductor materials across four programmatic areas: PFAS-free process chemicals, catalysts, rare earth-free magnets, and battery systems. Watch the video in comments to learn more from SandboxAQ's team. AI progress depends on next generation chips. In turn, those chips depend on the complex gauntlet of semiconductor materials used in the entire fab floor - process chemicals, catalysts, magnetic materials, precursors, coatings, and energy systems. These inputs then determine whether manufacturing can scale with the required precision, energy efficiency, yield, reliability, and cost. This milestone announcement reflects a critical, broader recognition that U.S. cannot build a durable semiconductor advantage by focusing solely on factories. We must also rebuild and onshore the upstream materials stack that makes advanced manufacturing possible. As computing demands rise, so does the burden on semiconductor materials. More performance, tighter process windows, greater energy intensity, more advanced packaging, and more complex integration all increase the need for better materials and faster innovation cycles. Historically, materials innovation has been one of the slowest links in the hardware chain. The search space is vast, striking a balance between trade-offs is brutal, experiments are expensive, and qualification takes immense rigor and time. The advantage of physics-based AI is that it drastically compresses this timeline. By utilizing SandboxAQ’s Large Quantitative Models (LQMs), we can bypass traditional trial-and-error. Our LQMs and ReAQT platform map molecular behaviors using physical laws, allowing us to screen millions of untried candidates virtually and focus experimental lab efforts strictly where they matter most. We are very excited to see this mission recognized at national scale. And now, the hard work of execution is ahead of us. Onward, in the build mode, with the most talented SandboxAQ team! 🏗️🚀 Nadia Harhen, Ang Xiao, Aayush R. Singh, Takeshi Yamazaki, Rahul G., Christopher Young, Jack Hidary, Andrew McLaughlin, David Silverman, Ben Yaffe, JP Murgueitio, Charlie O'Loughlin, Scott Healey, Deren Koseoglu, Suzannah Radack, Jennifer Stewart, MBA, Bret Janak, Bill Fitzgerald, Daniel Garden #semiconductors #aiforscience #materialsdiscovery #chipsact #manufacturing #innovation https://lnkd.in/g2tQ9YBWUS awards $500 million to Nvidia-backed SandboxAQ for finding new chipmaking materialsUS awards $500 million to Nvidia-backed SandboxAQ for finding new chipmaking materials
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Jack Hidary shared thisReuters covered the U.S. Department of Commerce new $500 million award to SandboxAQ focused on accelerating discovery across four critical materials categories: PFAS alternatives, catalysts, rare-earth-free magnets and batteries. Excited to see our large quantitative models (LQMs) having impact in this significant sector! Read the Reuters story: https://lnkd.in/gYCZfmVwUS awards $500 million to Nvidia-backed SandboxAQ for finding new chipmaking materialsUS awards $500 million to Nvidia-backed SandboxAQ for finding new chipmaking materials
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Jack Hidary shared thisHow do you break the decades-long battery supply bottleneck? By designing around these bottlenecks and moving to even better battery chemistries. That is exactly why SandboxAQ’s introduction of AQVolt26 is a critical step to increase US resilience in this key technology area. AQVolt is a frontier AI model purpose-built for battery materials discovery. Built on more than 320,000 high-accuracy quantum mechanical simulations and over 200 million atomic configurations, AQVolt26 combines large-scale scientific datasets with physics-informed AI to model battery materials under realistic operating conditions. A key breakthrough is its ability to accurately capture highly distorted, high-temperature atomic environments that are critical for understanding ion transport and degradation in solid-state batteries, conditions where many existing machine learning models struggle. As a result, AQVolt26 achieves exceptional robustness under extreme simulation conditions, with failure rates of approximately 0.2%, enabling long-timescale atomistic simulations that were previously impractical. By allowing researchers to rapidly screen and evaluate next-generation battery materials, including promising halide solid-state electrolytes, AQVolt26 has the potential to compress discovery timelines from years to months while substantially reducing experimental cost and development risk. https://lnkd.in/gPFtn3Fa Deep tech and frontier AI are moving beyond text and image generation. They are playing an active role to solve our hardest scientific and industrial challenges in the physical world.AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State BatteriesAQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
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Jack Hidary shared thisProud of the team for their new paper in the peer-reviewed journal Nature Computational Materials. The SandboxAQ team presents a breakthrough in catalyst discovery and computational chemistry. Catalysis drives the global economy, from the fuels that power our world to the materials that shape it. With our AQCat model, industries can now simulate, screen, and optimize catalysts with physics-based accuracy, unlocking performance and sustainability breakthroughs at unprecedented scale. The team built a dataset containing 13.5 million Density Functional Theory (DFT) calculations and found that training a model on specialized chemistry improved performance on the target domain but often caused forgetting of knowledge learned elsewhere. To address this challenge, the team trained on multiple datasets and provided the model with additional information about the underlying physics. Using this approach, the team improved performance without sacrificing generalization. As AI moves deeper into chemistry, materials science, energy systems, and drug discovery, success will depend on more than larger models and larger datasets. It will depend on how effectively we incorporate scientific knowledge while preserving the ability to generalize across domains. Check out the paper: https://lnkd.in/dM_ZwQWD
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Jack Hidary shared thisThe global AI race will not be won with chatbots that predict the next word. In today’s Wall Street Journal, I argue that staying ahead of China requires a new class of AI models built on physics, chemistry, and quantitative reasoning, not just language and images. China’s next five-year plan doubles down on AI, materials science, quantum technology, energy storage, and defense. These sectors run on equations, not prompts. Language models are wonderful productivity tools, but they are not designed to compute optimal alloy compositions, advance semiconductor design, or model high-dimensional financial risk. Pharma, semiconductors, energy, and financial services represent more than $25 trillion in global GDP. These industries require quantitative AI trained on lab data and the equations of the physical and quantitative worlds. Read the full op-ed in the WSJ here: https://lnkd.in/e_ygVkf3
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Jack Hidary liked thisJack Hidary liked thisCongratulations 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: https://lnkd.in/ekxSiKV7 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: https://lnkd.in/efsTe_dw
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Jack Hidary liked thisJack Hidary liked thisEarlier today we shared news of our Nature Communications paper with Baylor College of Medicine. C&EN 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: https://lnkd.in/eifJNG7k Read the paper: https://lnkd.in/ekxSiKV7 Baylor's press release: https://lnkd.in/efsTe_dw
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Jack Hidary liked thisJack Hidary liked thisSandboxAQ tests its AQNav magnetic navigation system on Northrop Grumman's Lumberjack drone. The companies said the system provides positioning without satellites or external signals, and was installed on the aircraft's existing onboard computers in less than an hour. Northrop Grumman was the mission systems integrator, while ESAero provided the aircraft and systems integration. Read more: https://lnkd.in/dPjpwGzz 📰 Subscribe to the weekly Robotics Brief Newsletter: https://lnkd.in/eGiCpMzH #ai #artificialintelligence #ainews
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Jack Hidary liked thisJack Hidary liked thisCelebrated the premiere of Star Trek's 'The Sixty-Year Mission' documentary yesterday in Los Angeles, with the original crew who transformed how I and countless others view and engage with the world. This was a glimpse of how transformative Moonshots can be, even if it seems like a long shot or just a crazy idea. I've said this before, but we live in a time where bringing ideas to life that can impact millions and billions of people is possible. This is the best time not only to become an entrepreneur but also to be an agent of change in your local community. Star Trek was my inspiration, and I hope competitions like the Build with Gemini XPRIZE and the Future Vision inspire many more great submissions. I have never been more optimistic about the future! Truly one of the best moments to be alive.
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Jack Hidary liked thisJack Hidary liked thisWe didn't build Switch for scientists. We built it so our software team could coordinate AI agents in Slack and Teams. But last week at our company offsite, Brian Cottle and Ethan Pratt from our medical sensing team got up and presented how they've been using Switch for their research. Brian had reached out a few weeks ago asking how to set it up, but seeing them present it to the whole company was something else. Their agents and team now work in the same Slack channels, no longer bouncing between tools, and things get done faster. At SandboxAQ we have teams building everything from navigation systems to drug discovery tools, and it turns out the agent coordination problem looks the same across all of them. Whether you're shipping software or running simulations, someone still ends up being the glue between their own agents. That's what Switch is for. Open source, self-hostable, works with whatever you already use. Is anyone else running into this same coordination problem across their teams? Give Switch a try: https://lnkd.in/g3Qmxy2y Flint AI / SandboxAQ
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Jack Hidary liked thisJack Hidary liked thisSandboxAQ 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
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Jack Hidary liked thisJack Hidary liked thisSandboxAQ 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!
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Jack Hidary liked thisKit Yee Au-Yeung is a super star with an amazing experience and a unique human being. Strongly recommend this one to understand more of SandboxAQ !Jack Hidary liked this🎙️I recently had an inspiring conversation with Kit Yee Au-Yeung, who leads SandboxAQ's team focusing on heart-health technology. Among the many things I enjoyed about our convo, I loved hearing how proud she is to watch her team of quantum physicists step out of their comfort zones and learn to navigate the complex world of medical device regulations as their product matures. 🚀 If you're interested in med tech, leadership, and startup culture, this is the episode for you! Full convo on YouTube: https://lnkd.in/gY2JXMYx
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