SandboxAQ Leverages NVIDIA DGX Cloud To Fuel Scientific Discovery

Channel Post MEA

16-04-2025

SandboxAQ has announced a collaboration with NVIDIA to accelerate breakthrough innovations across biopharma, chemicals, advanced materials, financial services, cybersecurity, navigation, and medical imaging. SandboxAQ, a member of the NVIDIA Inception program, is leveraging the NVIDIA DGX Cloud AI platform on Google Cloud to build a state-of-the-art Large Quantitative Model (LQM) platform, fueling AI-driven scientific discovery. SandboxAQ is now uniquely positioned to help customers tackle the most complex and consequential business challenges through its LQMs – delivering breakthroughs where traditional models have fallen short – while offering unparalleled speed, scalability, and precision.
This collaboration enables SandboxAQ to unlock critical breakthroughs specifically designed to transform customer outcomes, including: Up to 4x Faster Discovery Across Drug, Chemical, and Materials Pipelines: Accelerated by NVIDIA DGX Cloud, SandboxAQ replaces slow, resource-intensive design-make-test cycles with high-performance, equation-based simulations – reducing discovery timelines from months to weeks. Enhanced modeling capabilities support simultaneous optimization across multiple parameters, enabling faster validation of promising candidates and accelerating breakthroughs with greater confidence.
Accelerated by NVIDIA DGX Cloud, SandboxAQ replaces slow, resource-intensive design-make-test cycles with high-performance, equation-based simulations – reducing discovery timelines from months to weeks. Enhanced modeling capabilities support simultaneous optimization across multiple parameters, enabling faster validation of promising candidates and accelerating breakthroughs with greater confidence. Breakthrough Datasets, Curated with DGX Cloud: SandboxAQ is generating high-fidelity scientific datasets, combining chemical and biological simulations. These methods leverage equation-based LQM models to reveal interactions between small molecules and complex biological targets that were previously difficult to detect including conformer libraries for generative chemistry and synthetic affinity data for training predictive models. By powering causal knowledge graphs and more accurate molecular design, these datasets reduce false positives and improve success rates across the R&D pipeline.
SandboxAQ is generating high-fidelity scientific datasets, combining chemical and biological simulations. These methods leverage equation-based LQM models to reveal interactions between small molecules and complex biological targets that were previously difficult to detect including conformer libraries for generative chemistry and synthetic affinity data for training predictive models. By powering causal knowledge graphs and more accurate molecular design, these datasets reduce false positives and improve success rates across the R&D pipeline. Agentic AI Chemist – A New Era of Autonomous Discovery: SandboxAQ's AI Chemist combines and orchestrates multiple LQMs to transform the scale of the research and development process. It autonomously explores millions of potential chemical pathways, far beyond what a human chemist could evaluate, enabling the discovery of novel molecules and the optimization of compounds for clinical and scale up success.
Research Breakthroughs
Today's announcement builds on previous collaboration between SandboxAQ and NVIDIA: 2024 Breakthrough : SandboxAQ and NVIDIA achieved an 80x acceleration in quantum chemistry calculations using CUDA-accelerated Density Matrix Renormalization Group (DMRG), enabling accurate simulation of enzyme active sites and complex catalysts previously impossible due to computational limitations.
: SandboxAQ and NVIDIA achieved an using CUDA-accelerated Density Matrix Renormalization Group (DMRG), enabling accurate simulation of enzyme active sites and complex catalysts previously impossible due to computational limitations. 2025 Breakthrough : In the newly published joint research paper, 'Orbital Optimization of Large Active Spaces via AI-Accelerators,' for the first time, researchers successfully performed orbital optimization on a system with 82 electrons in 82 orbitals – more than doubling the size of simulations compared to previous works. This groundbreaking advance for GPU accelerated quantum chemistry calculations pushes the capabilities for molecular simulations into a regime that has thus far been entirely out of reach, with potentially far-reaching implications in catalysis, material science and high-dimensional parameter optimization.
Transformative Impact Across Key Customer Domains
SandboxAQ's enhanced capabilities deliver strategic outcomes across customer innovation and critical workflows: Biopharma and Healthcare : Proven track record of accelerating preclinical pipelines for pharma companies by rapidly generating and optimizing therapeutic candidates based on significantly improved predictability of drug efficacy and safety.
: Proven track record of accelerating preclinical pipelines for pharma companies by rapidly generating and optimizing therapeutic candidates based on significantly improved predictability of drug efficacy and safety. Chemicals and Materials : Enabling deeper, faster exploration and validation of sustainable chemical processes to unlock carbon and hydrogen utilization as well as next-generation energy storage technologies.
: Enabling deeper, faster exploration and validation of sustainable chemical processes to unlock carbon and hydrogen utilization as well as next-generation energy storage technologies. Cybersecurity and Strategic Infrastructure: Leveraging advanced modeling and predictive capabilities to enhance agility, strengthen resilience, and support proactive cybersecurity postures.
Pioneering How Organizations Can Effectively Leverage AI
SandboxAQ is pioneering a new category of enterprise AI through its proprietary Large Quantitative Models (LQMs), a platform specifically engineered to solve massively complex, high-stakes problems where precision and deterministic outputs are critical. Unlike generalized frontier models, SandboxAQ's LQMs are designed to reflect the underlying laws of physics, chemistry, biology, and economics – enabling outcomes that are not just predictive, but scientifically reliable. In fields like drug discovery, SandboxAQ trains LQMs on high-fidelity, domain-specific datasets to dramatically improve accuracy, reduce false positives, and accelerate the path from hypothesis to therapeutic insight. This focus on scientifically grounded, application-specific modeling sets SandboxAQ apart, empowering organizations to unlock value in areas where conventional AI simply cannot deliver.
'Our expanded work with NVIDIA accelerates our customers' ability to innovate and lead in their fields,' said Jack Hidary , CEO of SandboxAQ. 'By developing our platform on NVIDIA DGX Cloud and continuing our research collaboration, SandboxAQ will deliver a level of performance and insight that gives our customers a clear edge in accelerating innovation.'
'SandboxAQ is pushing the boundaries of AI-native science,' said Alexis Bjorlin , Vice President of NVIDIA DGX Cloud. 'NVIDIA DGX Cloud provides an AI development platform with essential scale and optimized application performance, empowering SandboxAQ to deliver cutting-edge capabilities and drive real-world impact for organizations tackling society's most critical challenges.' 0 0