SandboxAQ’s Quantitative AI models combined with the CUDA-DMRG algorithm accelerates computational chemistry calculations by 80x and catalyzes a new wave of breakthrough applications across industries
SandboxAQ announced a groundbreaking advancement that pushes the limits of computational chemistry, impacting fields such as biopharma, chemicals, materials science and other industries. Collaborating with NVIDIA, SandboxAQ leverages Large Quantitative Models (LQMs) and the NVIDIA CUDA-accelerated Density Matrix Renormalization Group (DMRG) algorithm. This allows scientists to perform highly accurate Quantitative AI simulations of real-life systems with exacting accuracy, going beyond what Large Language Models (LLMs) and other AI models can currently do.
Combining the CUDA-DMRG algorithm, the NVIDIA Quantum platform, and NVIDIA accelerated computing speeds up these highly accurate calculations more than 80x, compared with traditional 128-core CPU computations. At the same time, it more than doubles the sizes of computable catalysts and enzyme active sites calculated by the system. SandboxAQ researchers use these computational results to train AI networks to optimize for the desired treatment or catalyst as outlined in the preprint available at website.
“Advanced computing is opening new frontiers in scientific research. Our use of NVIDIA technology has allowed us to address some of the most challenging problems in chemistry,” said Dr. Martin Ganahl, senior staff scientist at SandboxAQ. “We are not only advancing our understanding of material science and chemistry, but also paving the way for the next wave of innovations in drug discovery and catalysis to tackle currently-untreatable conditions and find safer and cheaper ways to synthesize molecules and materials.”
“AI supercomputing is helping to solve critical problems in the chemical and pharmaceutical industries,” said Tim Costa, director of high-performance and quantum computing at NVIDIA. “SandboxAQ’s use of the NVIDIA Quantum platform is facilitating simulations at an unprecedented scale, enabling scientists to rethink what’s possible in computational chemistry.”
Also Read: MasterControl Makes GxPAssist AI Generally Available to Streamline Life Science Processes
“This work with NVIDIA underscores SandboxAQ’s commitment to pushing the envelope of scientific discovery and technological innovation,” said Jim Breyer, Founder and CEO of Breyer Capital and an early investor in SandboxAQ. “Unlocking the secrets of new compounds and catalysts makes possible a new era of LQM breakthroughs in various industries that take us beyond LLMs. This has significant implications for improving quality of life and driving economic growth.”
Last year, SandboxAQ announced AI collaborations with the University of California San Francisco (UCSF), NOVONIX, and Riboscience. In 2024, Flagship Pioneering, SPARK NS, and other organizations signed on to further their innovation pipelines.
Applications for LQMs range from biopharma to agriculture to advanced materials. In biopharma for example, the enzyme Cytochrome P450s plays a central role in human drug metabolism and is central to understanding drug toxicity. CUDA-DMRG can help solve the long-standing problem of accurately modeling cytochromes’ catalytic activity and provide a game-changing angle for computational toxicity prediction, allowing computational simulation to de-risk clinical trials before they happen.
Training large AI models with proprietary, generated data to unlock breakthroughs in the physical world is the heart of a new wave of Quantitative AI. LQMs can make accurate predictions about the world because they are grounded in exact, physics-based data. While LLMs are limited to the data available on the Internet or other existing sources, SandboxAQ‘s LQMs can access an unlimited supply of training data generated by physics-based Quantitative AI simulations.
Source: PRNewswire
