SandboxAQ, a startup focused on AI that emerged from Alphabet’s Google and is supported by NVIDIA, released a large set of data recently aimed at accelerating the identification of new medical therapies by assisting researchers in understanding how drugs attach to proteins. The objective is to enable scientists to predict whether a drug will bind effectively to its target within the human body, Reuters reported.

SandboxAQ is a B2B company that provides AI solutions to tackle significant global challenges. Its large quantitative models (LQMs) advance sectors like life sciences, finance, navigation, and cybersecurity. Founded in 2022 as an independent entity from Alphabet, SandboxAQ is backed by notable investors, including T Rowe Price, Eric Schmidt, and Marc Benioff. 

SandboxAQ raised $150 million from new investors including Google, NVIDIA and BNP Paribas. The investment has increased SandboxAQ’s Series E round to $450 million, valuing the startup at $5.75 billion. With this, SandboxAQ’s total funding has reached $950 million, with T Rowe Price Associates and Breyer Capital among prominent backers.

Google and NVIDIA have been ramping up their investments both internally and externally in quantum computing. This technology uses the concepts of quantum mechanics to execute calculations that exceed the limits of classical computers. Recently, NVIDIA, known for its chip processors that fueled the generative AI surge, has reportedly focused on ‘physical AI’.

The start-up also launched a new division in 2022 and has created large quantitative models (LQMs) capable of managing vast numerical datasets, executing intricate calculations, and performing statistical analyses. These models are accessible via both first-party and third-party platforms, including Google Cloud, and they have the potential to aid in drug discovery and the development of advanced financial models.

The Reuters report stated that, though genuine scientific experiments support the data, it was not obtained from a laboratory setting. Instead, SandboxAQ, which has secured nearly $1 billion in venture funding, created the data using NVIDIA’s processors and plans to integrate it into AI models that researchers can use to quickly assess whether a small-molecule pharmaceutical will attach to the targeted protein. This is a crucial question that must be resolved before a drug candidate can progress.

For instance, if a medication is designed to obstruct a biological process like disease advancement, researchers can use the tool to forecast whether the drug molecule will likely bind with the involved proteins in that process.

“We act as your strategic partner, seamlessly integrated into your programs to enhance your ability to generate novel molecular drug IP and clinical assets. Our focus is on optimising and applying specialised LQM solutions tailored to your specific drug discovery and development needs, while ensuring they are broadly applicable and reusable in future projects, delivering long-term value,” the company said on its website. 

This method represents an emerging sector that fuses traditional scientific computing methods with recent advancements in AI. In numerous disciplines, scientists have long had formulas that can accurately predict how atoms combine into molecules.

However, even with relatively small three-dimensional pharmaceutical molecules, the possible combinations become too extensive to calculate by hand, even with today’s fastest computers. Thus, SandboxAQ’s strategy was to leverage existing experimental data to compute around 5.2 million new, “synthetic” three-dimensional molecules, which were not encountered in the real world but derived from equations based on real-world data.

The synthetic data that SandboxAQ is making publicly available can be applied to train AI models that can predict whether a new drug molecule is likely to adhere to the targeted protein researchers are examining. It would do so in a fraction of the time it would take to compute it manually, while maintaining accuracy. SandboxAQ plans to monetise the AI models developed with this data, aiming to achieve results comparable to those obtained from actual lab experiments in a virtual format.

“This is a long-standing problem in biology that we’ve all, as an industry, been trying to solve for,” Nadia Harhen, general manager of AI simulation at SandboxAQ, told Reuters. “All of these computationally generated structures are tagged to a ground-truth experimental data, and so when you pick this data set and train models, you can actually use the synthetic data in a way that’s never been done before.”

While large language models (LLMs) have garnered media attention in recent years, SandboxAQ said on its website that they lack the capabilities to precisely simulate the physical world, leading to interesting yet inaccurate and unreliable outputs in the global economy’s largest disciplines. 

The company offers a fully integrated solution that encompasses the entire drug discovery and development lifecycle, ensuring efficiency and precision at every stage. 

From target discovery and hit identification to lead optimisation and toxicity prediction, the company provides comprehensive support that delivers actionable insights, enabling faster and smarter decision-making. Using its LQM-driven models, SandboxAQ can rapidly analyse multi-modal data to identify the most promising targets and candidates, thereby accelerating drug development.

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