#LargeQuantitativeModels

[govindhtech]

SandboxAQ LQMs In Cancer Detection & Treatment for SU2C

SandboxAQ and SU2C

Stand Up To Cancer and SandboxAQ Jointly Promote New Treatments

Today, SandboxAQ and SU2C announced a major partnership to identify and cure cancer. SandboxAQ's advanced Large Quantitative Models (LQMs) are used in SU2C-supported cancer research.

New cancer treatment methods at the earliest stage are the main goal of this program. The alliance uses SandboxAQ's LQM technology and other cutting-edge AI and data modelling technologies to accelerate disease therapy development.

This collaboration will aid high-impact cancer research in several key areas. These include finding hard-to-diagnose and treat tumours. The program will utilise predictive models to track disease recurrence and enhance patient response to therapy.

Researchers can use SandboxAQ's advanced computational techniques to detect sickness early warning signs. Additionally, they will be used to predict sickness and create more personalised patient care plans. Cancer research and treatment depend on SandboxAQ's AI LQM platform's complex biological system simulation.

Stand Up To Cancer president and CEO Julian Adams, Ph.D., thinks the alliance may change. He called it a “pivotal moment in cancer research” because we were at the “threshold of a new era” with new technologies that might identify and treat cancer earlier and more accurately. Dr. Adams noted that SandboxAQ's cutting-edge technology strengthens SU2C's scientific network and collaborative research model. He believes they can “turn breakthroughs in the lab into more lives saved” by working together.

SandboxAQ CEO Jack Hidary concurred, stating the business is delighted to battle cancer with its inventions. He added they will cooperate with SU2C to provide their AI LQM platform with researchers to study complicated biological systems. He believes this will accelerate cancer research to patient benefits. Hidary said this agreement brings it “closer to a future where more cancers are cured”.

This relationship successfully combines SandboxAQ's AI-driven discovery experience and SU2C's multi-institutional research leadership. The organisations use powerful computational methods in cancer research to advance precision medicine.

This project continues SU2C's commitment to cutting-edge tools and ambitious, collaborative research. SU2C accelerates promising cancer research using next-generation technology.

Over 3,100 scientists from 210 universities in 16 countries have received SU2C funding since 2008. SandboxAQ's technology's extensive reach and collaboration experience make it a good fit. SU2C's goal of reducing cancer mortality by 25% in five years and 50% in ten aligns with this collaboration.

To achieve this, early-stage cancer diagnosis must become the norm, and LQM technology focusses on this. SU2C, a 501(c)(3) nonprofit, raises money for research and awareness to cure all patients. Our scientific partner, the American Association for Cancer Research, manages and rigorously reviews research money.

SandboxAQ provides B2B quantum-AI solutions. Large Quantitative Models have helped biological sciences, financial services, and navigation. Top investors and strategic partners helped Alphabet Inc. found the corporation.

As funding and fundraising goals are completed, further information about the SU2C research teams participating in this relationship will be released.

[govindhtech]

LQMs for High-Performance Discovery of Next-Gen Materials

Accelerating Next-Gen Material Discovery with Large Quantitative Models

Automotive, consumer electronics, and aerospace companies compete to develop stronger, lighter, more efficient, and eco-friendly materials. Innovative material discovery has been laborious, trial-and-error, expensive, and error-prone. This often requires years of research, many prototypes, and thorough physical testing.

Large Language Models (LLMs) and other AI improvements have improved data processing and drawn conclusions from current literature, but they lack the deep scientific understanding needed for materials science advances. LLMs are good at knowledge management and workflow optimisation because they process and develop conclusions from existent material. LLMs lack the molecular interactions and physical laws understanding needed for drug and material discovery.

Presenting Large Quantitative Models

Large Quantitative Models (LQMs) are novel. Enterprise AI and AI-powered materials science are moving towards LQMs. Since they incorporate the fundamental quantum equations driving physics, chemistry, and biology, LQMs are aware of how molecules behave and interact, unlike LLMs.

For scientific discovery, LQMs use quantum-accurate simulations to predict chemical characteristics orders of magnitude more accurately than LLMs, which use textual input. They learn math, physics, chemistry, biology, and molecular interactions. LQMs model real-world systems.

When used with generative chemistry, LQMs can explore the entire chemical space for desired molecules. They can enable quantitative AI simulations that digitally examine how molecules or compounds respond billions of times before the most promising options are tested in labs or physical prototypes. Highly accurate synthetic data from these simulations is utilised to train LQMs, improving their speed, intelligence, and efficacy.

R&D Changes in Different Industries

Researchers and manufacturers benefit greatly from AI and quantum equations. Cutting R&D cycles from years to months or weeks speeds up time to market. LQMs improve projected accuracy and help researchers uncover novel materials, compounds, and alloys faster than standard models. Reduce lab experimentation to save money. They optimise production and produce eco-friendly products to enhance sustainability. These incentives boost innovation and help companies dominate.

Manufacturing executives may remain ahead of foreign competitors, reduce supply chain issues, and create new product and design possibilities by integrating LQMs into processes.

Real-World Applications Encourage

SandboxAQ, a leading LQM company, is influencing multiple sectors.

Discovering Alloys

With the U.S. Army Futures Command Ground Vehicle Systems Centre, SandboxAQ is changing alloy development. They detected five high-performing alloys utilising machine learning and high-throughput virtual screening from over 7,000 compositions. These alloys used less conflict minerals and reduced weight by 15% while maintaining strength and elongation.

Battery lifespan forecast:

By decreasing the end-of-life (EOL) projection time by 95%, SandboxAQ advanced lithium-ion battery research. Their low mean absolute error predicted EOL 35 times more accurately with 50 times fewer data. After training on almost a million hours of data, predictions can be made in six cycles. This might cut cell testing time from months to days and battery development time by four years.

Catalyst Design:

Together with DIC and AWS, SandboxAQ is revolutionising catalyst design. They found superior nickel-based catalysts by leveraging its QEMIST Cloud and high-performance computers to better predict catalytic activity. This discovery speeds up the search for effective, non-toxic, and affordable industrial catalysts by reducing computation time from six months to five hours.

Cleaner Energy:

The energy industry is transforming materials and chemical process optimisation with Aramco. SandboxAQ is using LQMs to construct a multi-GPU-enabled computational fluid dynamics solver to improve material design and process efficiency in oil and gas facilities to help Aramco lower its carbon footprint.

Discovery of drugs

AQBioSim from SandboxAQ correctly and scientifically models molecular behaviour using LQMs. It helps biopharma teams explore enormous chemical landscapes and speed up discovery by four times by simulating interactions, anticipating findings, and refining drug ideas in silico. This improves candidate quality, predicts toxicity and efficacy earlier, and cuts discovery time from years to weeks.

Materials and chemicals:

Teams can mimic the actual world with AQChemSim before entering the lab. Since it uses basic principles, it accurately predicts the behaviour of molecules, materials, and industrial systems in real life. Teams can forecast performance under duress, expedite formulation, and enhance sustainable practices to shorten development cycles.

Cybersecurity:

SandboxAQ's AQtive Guard platform uses LQMs to revolutionise identity and cryptography management at scale. Risk analysis, unprecedented visibility, automated remediation, deep, AI-powered insight, post-quantum cryptography readiness, and compliance speed are its priorities.

Different Domains:

Medical diagnostics, treatment planning, and navigation are improved by LQMs, increasing autonomy and accuracy.

Solving Expertise, Scalability, and Cost Issues

Manufacturing executives often complain about AI technology affordability, scalability, and competence. SandboxAQ addresses these challenges. Although integrating AI requires an initial investment, SandboxAQ's platforms save R&D costs by expediting discoveries and minimising lab costs. Businesses are seeing product development and efficiency advances.

Scalable platforms like the cloud-native AQChemSim allow manufacturers of all sizes to use high-performance computing resources to do quantum-accurate simulations, democratising material discovery. The AQtive Guard platform is intelligent, fast, and scalable.

SandboxAQ's platforms enable enterprises to get quantum-powered insights without quantum computing teams by using AI-driven solutions that need minimum expertise.

Future and Beyond Manufacturing

LQMs facilitate previously impossible innovations and transform how industries develop next-generation materials and solutions. By replacing trial-and-error testing with fast, multi-dimensional search, LQMs accelerate design and discovery. They can solve strategic material-related business difficulties because of their particular qualifications.

SandboxAQ said it will work with industry experts and set new benchmarks to maximise LQMs' potential in battery, catalytic, and semiconductor applications. LQMs are being used by the company and NVIDIA to accelerate industry advances.