Researchers today announce AdaptiveFlow, an AI-informed platform that can virtually screen billions of drug-like molecules with a 1,000-fold reduction in computational costs over existing methods. Developed and validated by scientists from St. Jude Children’s Research Hospital, University of Pavia, Dana Farber Cancer Institute and Harvard Medical School, AdaptiveFlow allows prohibitively expensive ultra-large virtual drug screens to be done routinely. The open-source platform was published in Nature Biotechnology.

The platform’s framework demonstrated linear scaling up to 5.6 million virtual central processing units (CPUs) - a new benchmark for cloud-based drug discovery - allowing billions of molecules to be screened without loss of efficiency. As proof of concept, the team identified potent inhibitors for existing and emerging cancer targets for which few inhibitors are known.

“AdaptiveFlow is the next generation in automated drug discovery platforms for routine ultra-large virtual screenings,” said co-corresponding author Christoph Gorgulla, PhD, Center of Excellence for Data-Driven Discovery, St. Jude Department of Structural Biology. “With this platform, we are able to screen 69 billion molecules, representing the largest ready-to-dock library in the world.”

The recent expansion of ultra-large molecule screening libraries provided a call to action for the drug discovery field to unlock their potential. While pioneering efforts saw success in billion-compound screens, AdaptiveFlow is the first of a new generation focused on efficiency, affordability and access. This is rooted in its economical approach to computation.

“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.”

At the core of AdaptiveFlow is an 18-dimensional grid in which each dimension represents a specific molecular property, such as molecular weight. This framework allows researchers to prioritize chemically diverse molecules and guides the rational selection of promising library subsets for deeper screening. A machine-learning classification model trained on these prescreening results then identifies the most promising molecules. These are then virtually screened against the protein target with over 1,500 supported docking protocols, which are subsequently ranked according to their predicted binding affinity.

“The initial hits are often of considerably better quality than traditional methods, which can save researchers much time and effort during the optimization phases of drug discovery,” Gorgulla said.

To demonstrate its potential, 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. In both cases, the platform identified inhibitors exhibiting binding strength comparable to pharmaceutically relevant standards.

“There are existing approved drugs on the market for PARP1, which made it a great benchmark for us, but FSP1 is a more challenging target because its binding site has an additional co-factor present,” Gorgulla explained. “We wanted to see if AdaptiveFlow also works in this complex setting and were excited to see it does.”

The successful identification of FSP1 inhibitors underscores 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.

“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.”

AdaptiveFlow is available for download at https://adaptive-flow.ai and https://github.com/QuantumAI4Bio

Cecchini D, Nigam A, Tang M, Reis J, Koop M, Gottinger A, Nicoll CR, Wang Y, Jayaraj A, Çınaroğlu SS, Törner R, Malets Y, Gehev M, Das KMP, Churion K, Kim J, Thomas N, Li Y, Seo HS, Dhe-Paganon S, Secker C, Haddadnia M, Hasson A, Li M, Kumar A, Levin-Konigsberg R, Choi EB, Shapiro GI, Cox H, Sebastian L, Braithwaite C, Bashyal P, Radchenko DS, Kumar A, Yang L, Aquilanti PY, Gabb H, Alhossary A, Wagner G, Aspuru-Guzik A, Moroz YS, Kalodimos CG, Fackeldey K, Schuetz JD, Mattevi A, Arthanari H, Gorgulla C.
AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.
bioRxiv [Preprint]. 2026 Mar 23:2023.04.25.537981. doi: 10.1101/2023.04.25.537981