SandboxAQ’s new tool ranks drug candidates for just $1 per 1,000

SandboxAQ has launched AQPotency, a new tool that ranks potential drug candidates for as little as $1 per 1,000 comparisons, significantly reducing the cost of older virtual screening methods. The Large Quantitative Model predicts how well a drug will work even without a detailed structural map of the disease target, overcoming a major obstacle in early discovery where many promising programs stall.

“AQPotency has given us and our customers a faster, scalable, and reliable way to prioritize compounds,” said Andrea Bortolato, Vice President of Drug Discovery at SandboxAQ, “without needing a 3D crystal structure of the target.” Unlike previous tools, AQPotency also reports the confidence level of each prediction, indicating when scientists can reliably use the results.

AQPotency Predicts Drug Efficacy Without Structural Data

This price point substantially lowers the cost of older virtual screening methods, which often require significant computational resources and time. The tool’s core innovation is its ability to predict a drug’s efficacy without requiring a solved protein structure of the target, a longstanding bottleneck in pharmaceutical research, SandboxAQ says. Many promising drug targets lack this crucial structural data, previously halting programs before they could begin.

Beyond cost savings, AQPotency addresses a critical limitation of earlier predictive tools; it doesn’t simply provide a score indicating potential efficacy, but also reports a confidence interval for each prediction. This confidence reporting allows scientists to assess the reliability of the score, a feature absent in many previous systems that delivered single, often unreliable, numbers.

The model’s versatility extends beyond predicting efficacy for a given target; it can also scan a broad range of proteins to identify those most likely to interact with a specific molecule. This reverse capability is particularly valuable when a molecule demonstrates a beneficial effect but the underlying mechanism remains unknown, offering researchers a focused set of leads for further investigation.

Professor Dario R. Alessi, OBE, FMedSci, FRS, Director of the MRC Protein Phosphorylation Unit at the University of Dundee, highlights the model’s impact on his team’s work. He said that SandboxAQ’s models have been very impactful, as they develop new treatments for Parkinson’s, and that the models “enable us to explore a much larger biochemical space in a short timeframe and improve both activity and selectivity.” Robin Roehm, CEO and Co-Founder at Apheris, emphasizes the practical benefits of the technology, stating, “What’s compelling about AQPotency is that it makes high-value discovery decisions faster and more practical,” and that it allows researchers to “prioritize the most promising compounds with greater confidence, focus experimental resources where they matter most, and expand discovery efforts to targets that have traditionally been harder to pursue.”