SandboxAQ announced the integration of its Large Quantitative Models (LQMs) with Claude, Anthropic's frontier AI model, making it possible to directly connect a large language model to a large quantitative model for drug discovery and materials science. Trained on real-world lab data and scientific equations, LQMs are AI models engineered for the quantitative economy, a $50+ trillion sector spanning biopharma, financial services, energy, and advanced materials.
Until now, running the advanced models for new drug discovery and materials discovery required specialised scientists and the ability to write complex code. With Claude serving as a natural language interface to SandboxAQ's LQM platform, any user can access that same capability through plain-English prompts, moving faster from hypothesis to discovery, in the physical world.
SandboxAQ's LQMs are accelerating drug discovery and materials science, with active programs underway at major pharmaceutical companies and demonstrated advances in battery chemistry, catalysts, and alloys. Several SandboxAQ's frontier models, such as AQAffinity and AQCat have been developed in collaboration with NVIDIA. SandboxAQ's platform also powers AI-native cybersecurity, medical research, and navigation systems, with financial services and risk modelling modules going live soon.
SandboxAQ builds its proprietary LQMs from the ground up, generating its own physics-grounded training data through high-fidelity simulations, including quantum chemistry calculations, molecular dynamics, and microkinetics, targeting the specific chemistries and conditions that matter most. SandboxAQ can augment these data sets with data from lab experiments. SandboxAQ trains its own AI models on that data, owns them outright, and connects them into automated workflows that run full design, test, and decision cycles, allowing users to move from question to defensible answer without writing a single line of code.
As of today's integration with Claude, users can access AQCat Adsorption Spin. AQCat allows users to lock in the most critical first step of any catalyst discovery workflow, adsorption energy calculation (a measure of how strongly molecules bind to a catalyst surface), allowing them to rapidly identify and prioritise the most promising candidates before committing costly modelling and lab resources to full-scale evaluation. AQCat Adsorption Spin gives users gold-standard accuracy at a fraction of the time and cost, unlocking materials screening at a scale that was previously out of reach.
Catalysts underpin more than 90 per cent of all commercially produced chemical products, and the ability to screen at unprecedented speed and accuracy has a direct impact across green hydrogen, sustainable aviation fuel, fertiliser production, plastics recycling, and more.
"SandboxAQ's integration with Claude removes one of the key barriers between a researcher's scientific intuition and rigorous physics-grounded computation, accelerating discovery across energy materials and beyond," said Partha P. Mukherjee, PhD, Professor & University Faculty Scholar, School of Mechanical Engineering, and Director, Centre for Advances in Resilient Energy Storage (CARES), Purdue University.
"Now, researchers can access frontier physics-based models directly inside the AI tools they already use, with no additional infrastructure, code or barriers," said Jack D. Hidary, CEO of SandboxAQ. "Our LQMs bring the rigour of first-principles quantum chemistry to a conversational interface, and that changes how fast a user can move from question to answer across chemistry, materials science, and drug development."
