Modeling and Shared Compute Reveals Pathways of Synthetic Drug

By Bio-IT World Staff

December 12, 2025 | Synthetic cannabinoids, or new psychoactive substances (NPS), are synthesized versions of natural cannabinoids meant to target human cannabinoid receptor 1 (CB1) and serve as potential therapeutics, but they don’t behave like natural cannabinoids. Instead, these drugs have pronounced and dangerous physiological side effects, and the chemical diversity of the NPS structures makes them harder to control for drug enforcement agencies.

“Structural diversity in every component, while maintaining high binding affinity and potency for CB1 make these molecules easier for drug manufacturers and harder to ban by drug enforcement agencies,” write the authors of new research published in eLife (DOI: 10.7554/eLife.98798.3)   

One clue as to why seems to lie in the pathway the drugs trigger. When natural and classical cannabinoids bind to CB1 they tend to launch the G-protein signaling pathway. NPSs bind to CB1 but launch the β-arrestin signaling pathway instead. This switch in signaling seems linked to more severe psychological effects.

Researchers from the University of Illinois Urbana-Champaign used deep learning and large-scale computer simulations to identify structural differences in synthetic cannabinoid molecules that cause them to bind to human brain receptors differently from classical cannabinoids. They compared ligand-protein interactions of a representative NPS (MDMB-FUBINACA) and a classical cannabinoid (HU-210) by studying their unbinding mechanism and downstream signaling.

Binding Challenges

“New psychoactive substances bind very strongly to cannabinoid receptors in the brain and are slow to unbind, making them difficult to observe and simulate in standard laboratory or computer experiments,” said chemical and biomolecular engineering professor Diwakar Shukla in a press release. “It can take a huge amount of computer time to see these rare binding and unbinding events.”

The team chose two approaches to addressing this challenge: Transition-Based Reweighting Method, to estimate the thermodynamics and kinetics of slow molecular processes, and Folding@Home to run many compute-intensive simulations in parallel.

In the lab, graduate student Soumajit Dutta used the Transition-Based Reweighting Method (TRAM) of simulation to estimate the thermodynamics and kinetics of slow molecular processes. The team found that TRAM can also be used to observe the rare, slow molecular processes involved in the unbinding of NPS from cannabinoid receptors by efficiently sampling these events that would otherwise require massive computing resources.

To get computing resources, the researchers enlisted the Folding@Home platform, a 2021 Bio-IT World Innovative Practices Honorable Mention project that lets millions of volunteers worldwide donate computing power. This approach allowed the team to run many simulations in parallel, stitching the results together and using algorithms to decide which simulations to run next. It allows for the study of very long or rare events that would be nearly impossible with a single computer or a small cluster.

Modeled Revelations

“TRAM estimated thermodynamics helped to decipher the differences between the unbinding of NPS MDMB-FUBINACA and classical cannabinoid HU-210,” the authors wrote.

Among their observations they found that for MDMB-FUBINACA, a larger conformational change is observed within the pocket. For HU-210, conserved cyclic group leads to the dissociation from the receptor, supporting previous simulation where the alkyl side chain of the ligand binds to the receptor first. Major differences in protein-ligand interactions were observed in transmembrane domain TM7. Stronger interactions were observed for HU-210 than MDMB-FUBINACA.

“This interaction pattern was consistent across other NPS and classical cannabinoids, indicating a universal difference in how these two groups of compounds interact with TM7,” the authors write.

With more details about why NPSs signal pathways associated with more adverse effects, researchers can focus on designing new molecules that avoid triggering these pathways for medical use. Shukla said their findings could direct more researchers to aim for compounds that bind less tightly or unbind more readily, potentially reducing the drugs’ harm.