Scientists have introduced AdaptiveFlow, a new AI-driven tool capable of evaluating vast numbers of potential drug compounds at a fraction of the usual computing expense—cutting costs by up to 1,000 times compared to traditional techniques. Created and tested by researchers from St. Jude Children’s Research Hospital, the University of Pavia, Dana-Farber Cancer Institute, and Harvard Medical School, the platform makes it feasible to conduct massive virtual screenings that were previously too costly to perform regularly.
The findings were shared in Nature Biotechnology, where the team highlighted that their system scales efficiently across computing resources, handling up to 5.6 million virtual CPUs in a straight-line progression—a record for cloud-based drug discovery. This capability lets researchers analyze billions of molecules without sacrificing performance.
Platform Capabilities
AdaptiveFlow identified potent inhibitors for existing and emerging cancer targets for which few inhibitors are known. As proof of concept, the team used AdaptiveFlow to find inhibitors for a well-known anticancer target, poly(ADP-ribose) polymerase 1 (PARP1), and an emerging target, ferroptosis suppressor protein 1 (FSP1), which is involved in cell death and has recently been linked to cancer cell survival.
‘This marks a major leap forward in automated drug discovery, enabling routine large-scale virtual screenings,’ stated co-lead author Christoph Gorgulla, PhD, of St. Jude’s Center of Excellence for Data-Driven Discovery. ‘With AdaptiveFlow, we’re now able to assess 69 billion molecules, the largest ever ready-to-use collection for this purpose.’
Efficiency and Accessibility
The growing availability of massive molecular libraries has pushed the field to find better ways to harness their potential. While earlier efforts achieved success with billion-compound tests, AdaptiveFlow stands out as the first system designed with cost-effectiveness, affordability, and broad accessibility in mind, thanks to its streamlined computational approach.
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“A lot of software loses communication efficiency with increasing CPU count,” Gorgulla explained, “but with AdaptiveFlow, the scaling behavior is perfectly linear, even with millions of CPUs. That is special.”
The system operates through an 18-dimensional grid, where each axis corresponds to a distinct molecular trait, such as weight. This structure helps researchers focus on chemically varied compounds and intelligently narrow down the most promising candidates for further testing.
Impact and Availability
The successful identification of FSP1 inhibitors shows AdaptiveFlow’s potential as a powerful tool for modern drug discovery. By democratizing access to ultra-large molecule libraries, faster and more impactful therapeutic development can follow. AdaptiveFlow is available for download at adaptive-flow.ai and github.com/QuantumAI4Bio.
“We have step-by-step guidance and tutorials available, and the entire platform is open source, so that everyone can use it for free,” Gorgulla said. “We wanted to make ultra-large virtual screening as accessible as possible so it can translate into better molecules for more challenging target proteins that can really help improve clinical success rates.”
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