SandboxAQ, an artificial intelligence startup backed by Nvidia (NASDAQ:NVDA) and born from Alphabet’s (NASDAQ:GOOG, GOOGL)) Google, has released a dataset containing 5.2 million synthetic molecules aimed at transforming how scientists predict drug-protein binding, Reuters reports.
Rather than generating the data through lab experiments, SandboxAQ used Nvidia chips and scientific computing methods to create synthetic 3D molecules, using physics-based equations grounded in real-world data. According to Reuters, the goal is to significantly reduce the time associated with discovering new drugs.
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SandboxAQ is helping scientists tackle a critical challenge in drug development: predicting whether a small-molecule drug will bind to a protein target. While this process traditionally involved complex and time-consuming methods, SandboxAQ’s AI models, trained on synthetic data, can generate predictions in a fraction of the time required by manual calculation, Reuters says.
The 5.2 million structures were generated by simulating how atoms combine to form molecules, using verified equations and a foundation of experimental data. According to Reuters, these structures are computationally created but tagged to real-world results, offering a bridge between traditional drug development and machine-learning approaches.
Scientists can use SandboxAQ’s data-driven tools to predict whether a drug molecule designed to disrupt a disease-related process will bind to its intended protein target, Reuters reports.
The approach blends established scientific computing with recent advancements in artificial intelligence, forming a hybrid method that enables faster and more precise molecular modeling. While researchers have long used equations to understand how atoms form molecules, Reuters says that SandboxAQ’s integration of AI adds scalability to what was once a tedious process.
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According to Nadia Harhen, SandboxAQ’s general manager of AI simulation, this method allows researchers to use synthetic data in new ways. Harhen emphasized the industry-wide challenge this data intends to solve.
