NVIDIA supports the AI pharmaceutical revolution, SandboxAQ synthetic data solves the drug screening dilemma

According to Zhitong Finance APP, SandboxAQ, an AI startup spun off from Google's parent company Alphabet (GOOGL.US) and strategically supported by NVIDIA (NVDA.US), officially released a large-scale synthetic dataset on June 18 local time, aimed at accelerating the global new drug development process by simulating the interaction mechanisms between drug molecules and proteins. This tech newcomer, which has raised nearly $1 billion in funding, is attempting to break the temporal and spatial limitations of traditional laboratory research and reconstruct the underlying logic of drug screening using computing power. Unlike the traditional path that relies on physical experiments to obtain data, SandboxAQ innovatively integrates computational chemistry with artificial intelligence. The company's algorithm platform, built on NVIDIA's high-performance chips, generates 5.2 million three-dimensional molecular structures that have not yet been observed in the real world by solving quantum mechanical equations that describe the forces between atoms. These "virtual molecules," although not synthesized in the laboratory, strictly follow the physical laws in their spatial configuration and chemical properties, effectively creating a vast molecular library in the digital world. "This addresses a core pain point in drug development that has persisted for decades." Nadia Hahen, head of AI simulation business at SandboxAQ, revealed that when researchers identify a disease-related protein as a drug target, they can quickly screen theoretically potential candidate molecules through the platform. Compared to the limitations of traditional computer-aided drug design, which can only handle a limited number of molecular combinations, the newly released synthetic dataset can improve predictive efficiency by several orders of magnitude, and the predictive results have reached laboratory standards in terms of alignment with real biological experiments. This innovative paradigm is reshaping the early stages of drug development. For example, in cancer treatment, if a research team attempts to block a key protein that promotes the proliferation of cancer cells, traditional methods require synthesizing and testing thousands of molecules in the laboratory, a process that can take years. With SandboxAQ's technology, researchers can directly simulate billions of molecules interacting with target proteins in virtual space, compressing the screening cycle to a few weeks and significantly reducing the time and financial costs of new drug development. It is worth noting that while the company offers the synthetic dataset for free to academic institutions, it commercializes the AI predictive models trained on this data. This "data open-source + model charging" hybrid model not only promotes foundational research in the industry but also builds a sustainable technological barrier. As the biopharmaceutical industry continues to increase its investment in AI drug development, this Silicon Valley-born tech force is attempting to carve out a new path in the trillion-dollar pharmaceutical research and development market