The company SandboxAQ thinks the real bottleneck isn’t the AI models themselves; it’s the interface. Now, it has done something that could change how scientific research works: it integrated its physics‑grounded AI models directly into Anthropic’s Claude chatbot. Researchers anywhere, regardless of their programming ability, can ask questions in simple language and run quantum chemistry calculations as if they were chatting with a colleague.
SandboxAQ describes this move as an effort to expand access to the “quantitative economy” - a fifty‑trillion‑dollar sector spanning biopharma, energy, finance and advanced materials. For India’s fast‑growing biotech community, that is the most important number in this entire announcement.
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What Did SandboxAQ Actually Do?
SandboxAQ is a five‑year‑old company that began as a spinout from Alphabet, the parent company of Google. Eric Schmidt, Google’s former CEO, chairs its board. The company has raised more than nine hundred and fifty million dollars and built several business lines including a cybersecurity unit. Its most distinctive technology is called Large Quantitative Models, or LQMs.
Unlike standard large language models that learn patterns from text, LQMs are “physics‑grounded” – they are built on scientific equations and the actual laws of the physical world. They can run quantum chemistry calculations, simulate molecular dynamics, and study microkinetics, which is the study of how chemical reactions unfold at the molecular level. This tells researchers how candidate molecules are likely to behave before anyone steps into a laboratory.
“Trained on real‑world lab data and scientific equations, LQMs are AI models engineered for the quantitative economy,” the company said in a release. Previously, users of SandboxAQ’s LQMs would have had to provide their own digital infrastructure to run the models. With the integration into Claude, they can now access the same capability through plain‑English prompts. The company also uses the Model Context Protocol (MCP) to make this possible, directly connecting a frontier language model to a frontier quantitative model.
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Why the Interface Matters as Much as the Science
There is a common failure mode in the world of scientific AI tools. Companies build models that work brilliantly on paper, but the software is so clunky that only computational scientists can use it. SandboxAQ is betting that lowering the barrier to entry will be just as important as improving the model’s accuracy.
Nadia Harhen, SandboxAQ’s general manager of AI simulation, explained the customer profile. “Our customers come to us because they’ve tried all the other software out there, and the complexity of their problem is such that it didn’t work or didn’t yield positive results for them when that translation went to take place in the real world,” she told TechCrunch.
“For the first time, we have a frontier [quantitative] model on a frontier LLM that someone can access in natural language,” Harhen added. Her remark summarises the core of this partnership. It is not just about having a better quantitative model. It is about putting that model into a chat interface that researchers already know how to use.
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What India Can Learn From This Shift
India is already a global hub for pharmaceutical manufacturing and generic drug production. It is now also trying to become a centre for AI‑driven drug discovery. Several Indian startups are working in this space.
Bengaluru‑based Peptris Technologies, for example, has built an AI‑led discovery engine that generates novel molecules and predicts critical drug development parameters early in the process. In February 2026, it raised seventy crore rupees in Series A funding co‑led by IAN Alpha Fund and Speciale Invest. The company is advancing programmes toward clinical readiness in areas including rare diseases, inflammation, oncology, and women’s health. Peptris follows a B2B engagement model, working closely with pharma and biotech partners to license assets and co‑develop programmes.
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Other Indian players include Bugworks Research, Pandorum Technologies, Elucidata, and DeepCure.ai. At the BioAsia 2026 conference in Hyderabad, a deep‑tech startup called BioVaram showcased its AI‑powered regenerative medicine platform, including a DAD Kit for faster and more accurate apoptosis detection to aid cancer research.
The Indian government is also paying close attention. At the India Pharma 2026 conference, policymakers and industry leaders agreed that AI will play a pivotal role in accelerating drug discovery. The Department of Pharmaceuticals organised a webinar in April 2026 on AI‑driven innovations, covering everything from target identification and lead optimisation to AI‑enabled trial design and regulatory decision support.
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The Regulatory Angle Nobody Is Speaking About
India has a new data protection law: the Digital Personal Data Protection (DPDP) Act, 2023, along with the DPDP Rules, 2025. The Act applies to any entity processing digital personal data in India, including AI health startups, genomics platforms, biobanks, and research institutions that use AI for drug discovery.
What makes this relevant to the SandboxAQ announcement is that drug discovery increasingly relies on large‑scale genomic and molecular data. Under the DPDP framework, such data cannot be processed without clear legal justification and, in many cases, fresh explicit consent – especially when data collected for one purpose (for example, diagnostic testing) is later repurposed for AI model training, drug development partnerships, or commercial analytics. This creates an additional layer of governance that Indian companies must navigate.
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A Gap That Still Remains
SandboxAQ’s models are powerful. They are also built for a global quantitative economy. For Indian researchers who want to work on local problems – such as finding new treatments for diseases that are particularly common in the Indian population, or repurposing existing drugs for neglected tropical diseases – the ability to access these models through a conversational interface is a clear advantage. But there is a gap.
The models are based on scientific equations and real‑world lab data from global sources. They are not yet fine‑tuned for India’s specific chemistry infrastructure, manufacturing conditions, or demographic profiles. That gap will eventually need to be filled by local AI research.
SandboxAQ has already demonstrated active programmes in battery chemistry, catalysts, and alloys, in collaboration with NVIDIA. Several of its frontier models, such as AQAffinity and AQCat, have been developed with NVIDIA. AQCat, now accessible through Claude, accelerates the most critical first step in any catalyst discovery workflow: adsorption energy calculations. This kind of tool could be directly useful for researchers in India working on green hydrogen, sustainable aviation fuel, or fertiliser production.
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What You Can Do
If you are a researcher, a student in biotechnology or chemistry, or a founder working on AI‑driven drug discovery, you now have a new tool that requires no specialised computing infrastructure. Instead of waiting for access to high‑performance computing clusters, you can open a Claude chat and start asking questions. You can explore how a molecule might behave, compare binding affinities, or simulate reaction pathways using natural language.
For Indian companies, this is also a moment to assess their internal capabilities. Are your computational scientists still spending most of their time building infrastructure instead of doing science? Could a tool like this accelerate your discovery timelines? The cost of experimenting with these models is now low enough to find out.
The integration also opens the door for new kinds of educational use. If students can run quantum chemistry simulations through a chatbot, the way chemistry is taught could begin to shift away from memorisation and toward interactive problem‑solving.
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The Bottom Line
Drug discovery has always been a war of attrition. Most candidates fail. The ones that succeed take too long and cost too much. AI has long promised to change this arithmetic, but the tools themselves have remained out of reach for many scientists.
SandboxAQ’s integration with Claude is not a guarantee of faster cures. It is a signal that the next wave of scientific AI will be judged not only by accuracy but also by accessibility. The models will become more powerful. The interfaces will become simpler. And eventually, the question will shift from “who can run the model” to “who can ask the right question”.
For India, with its large pool of scientific talent, growing biotech ecosystem, and evolving regulatory framework, that is a question worth answering soon.
Are you working in AI‑driven drug discovery or biotech in India? Would a conversational interface like this help you run simulations faster? Share your thoughts in the comments below.
If you found this breakdown useful, share it with a colleague in pharma, biotech, or academic research. The tools are changing. The way we work needs to change with them.
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FAQ
Q: What exactly does SandboxAQ’s integration with Claude allow me to do?
A: It lets you access Large Quantitative Models (LQMs) for drug discovery and materials science through natural language prompts inside Claude. Instead of setting up specialised computing infrastructure or writing code, you can ask questions about molecular behaviour, run quantum chemistry calculations, and simulate chemical reactions as if you were chatting with an assistant.
Q: Do I need a PhD in computational science to use this?
A: No. That is precisely the point of the integration. The models are designed for researchers who may not be computational specialists. You need to understand the scientific question you are asking, but you do not need to set up servers, write scripts, or manage complex workflows.
Q: Are there any Indian companies already working in this space?
A: Yes. Peptris Technologies in Bengaluru raised seventy crore rupees in Series A funding in February 2026. Other Indian players include Bugworks Research, Pandorum Technologies, Elucidata, and DeepCure.ai. At BioAsia 2026, the startup BioVaram showcased AI‑powered regenerative medicine tools.
Q: How does India’s DPDP Act affect AI drug discovery?
A: The DPDP Act applies to any entity processing digital personal data of individuals in India. In the context of drug discovery, this includes genomic data, patient data used for research, and even molecular data that might be linked to identifiable individuals. Biotech companies and AI health startups must ensure they have proper consent, purpose limitation, and data governance in place.
Q: Can this technology be used for diseases specific to the Indian population?
A: In principle, yes. But the models are currently trained on global data. For the best results on India‑specific problems, researchers would need to fine‑tune the models or supplement them with local laboratory data and clinical insights.
Q: Is this replacing traditional drug discovery methods?
A: No. The models accelerate specific steps in the discovery process, such as predicting molecular behaviour, calculating binding affinities, and screening catalysts. They do not replace the need for laboratory experiments, clinical trials, or regulatory approvals. The goal is to reduce the time and cost of early‑stage research, not to eliminate the human scientist.
Q: How can I access SandboxAQ’s models through Claude?
A: The integration is currently being rolled out. Check Anthropic’s documentation or SandboxAQ’s website for the latest access instructions. The company has stated that the models are now available through Claude, so users should be able to start experimenting through the standard chat interface.
Tags: SandboxAQ, Claude AI, Drug Discovery India, Anthropic, Large Quantitative Models, Indian Biotech
discovery and materials science.
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