RESEARCH: CANNABINOID
FOLDING PROJECT #19012 PROFILE
PROJECT TEAM
Manager(s): Soumajit DuttaInstitution: University of Illinois Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 90,246Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project compares how two types of synthetic marijuana, MDMB-Fubinaca (non-classical) and HU-210 (classical), interact with cannabinoid receptors. The goal is to understand why non-classical cannabinoids cause more harmful side effects than classical ones.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Cannabinoid Receptors Cannabinoid receptors (CBs) are part of the endocannabinoid signaling system, which help to maintain homeostasis in neuron signaling to control pain, obesity, and other neurological disorders.
Therefore, synthetic cannabinoids (SCs) were designed and tested to target CBs as potential therapeutical selective drugs.
Initially, SCs were designed by modulating the scaffolds of known phytocannabinoids.
However, chemically diverse synthetic cannabinoids (Novel Psychoactive Substance (NPS)) were
discovered rapidly, which have a high affinity towards CBs and significantly modulate the receptor activities.
These molecules were started to get sold in the market as abusive drugs under different brand names (e.g., K2, spice) and caused thousands of hospitalizations of patients across the US due to more adverse effects, including impairment of fine motor skills and increased blood pressure, tachycardia.
It is hypothesized that β-arrestin biased downstream signaling of these NPSs causes more adversarial effects compared to classical cannabinoids.
However, how these classical and non-classical cannabinoids affect receptor conformational dynamics distinctly,
has not been mechanistically studied.
In this project, we compare the unbinding mechanism and kinetics of a non-classical cannabinoid, MDMB-Fubinaca, and a classical cannabinoid, HU-210, using biased and unbiased simulation.
RELATED TERMS GLOSSARY AI BETA
Cannabinoid Receptors
Proteins in the body that bind to cannabinoids.
Cannabinoid receptors are proteins found throughout the body that respond to cannabinoids like THC and CBD. They play a role in regulating various functions, including pain perception, appetite, mood, and memory.
Endocannabinoid Signaling System
A complex system of neurotransmitters and receptors that regulates various bodily functions.
The endocannabinoid system is a network of chemicals and receptors throughout the body that helps maintain balance and regulate processes like appetite, mood, sleep, and pain perception.
Synthetic Cannabinoids
Man-made substances that mimic the effects of natural cannabinoids.
Synthetic cannabinoids are artificial chemicals designed to produce similar effects to cannabis. They often have unpredictable and potentially harmful consequences due to their varying potency and unknown long-term effects.
Novel Psychoactive Substance (NPS)
New psychoactive substances are synthetic drugs designed to mimic the effects of other drugs.
NPSs are a rapidly evolving category of drugs that often bypass legal restrictions. They can produce unpredictable and dangerous effects because their chemical structures and long-term impacts are poorly understood.
MDMB-Fubinaca
A synthetic cannabinoid with high affinity for CB1 receptors.
MDMB-Fubinaca is a potent synthetic cannabinoid that has been linked to severe health problems and hospitalizations. It acts by binding to the CB1 receptor in the brain, mimicking the effects of THC.
HU-210
A classical cannabinoid with high affinity for CB1 receptors.
HU-210 is a synthetic cannabinoid that was originally developed as a research tool. It binds strongly to the CB1 receptor in the brain and is known to produce psychoactive effects.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:26:20|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 8,180,583 | 278,858 | 29.34 | 0 hrs 49 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,951,799 | 264,684 | 26.26 | 0 hrs 55 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,337,949 | 256,328 | 24.73 | 0 hrs 58 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,655,070 | 243,386 | 23.23 | 1 hrs 2 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,605,517 | 231,249 | 19.92 | 1 hrs 12 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,371,785 | 226,956 | 19.26 | 1 hrs 15 mins |
| 7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,273,301 | 225,871 | 18.92 | 1 hrs 16 mins |
| 8 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,056,230 | 220,076 | 18.43 | 1 hrs 18 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,797,186 | 215,461 | 17.62 | 1 hrs 22 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,813,646 | 188,942 | 14.89 | 1 hrs 37 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,287,232 | 183,428 | 12.47 | 1 hrs 55 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,994,681 | 175,124 | 11.39 | 2 hrs 6 mins |
| 13 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,966,006 | 174,583 | 11.26 | 2 hrs 8 mins |
| 14 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,651,648 | 154,265 | 10.71 | 2 hrs 14 mins |
| 15 | GeForce RTX 2070 SUPER Mobile / Max-Q TU104M [GeForce RTX 2070 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 1,483,060 | 159,464 | 9.30 | 2 hrs 35 mins |
| 16 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,416,935 | 161,227 | 8.79 | 2 hrs 44 mins |
| 17 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,416,102 | 152,839 | 9.27 | 2 hrs 35 mins |
| 18 | Tesla P100 16GB GP100GL [Tesla P100 16GB] 9340 |
Nvidia | GP100GL | 1,387,860 | 124,155 | 11.18 | 2 hrs 9 mins |
| 19 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,313,491 | 151,917 | 8.65 | 2 hrs 47 mins |
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|
|||||||
| 20 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,308,351 | 150,521 | 8.69 | 2 hrs 46 mins |
| 21 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,254,696 | 147,773 | 8.49 | 2 hrs 50 mins |
| 22 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,172,940 | 140,447 | 8.35 | 2 hrs 52 mins |
| 23 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,077,953 | 141,799 | 7.60 | 3 hrs 9 mins |
| 24 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 892,182 | 134,009 | 6.66 | 3 hrs 36 mins |
| 25 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 824,556 | 126,842 | 6.50 | 3 hrs 42 mins |
| 26 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 767,043 | 128,406 | 5.97 | 4 hrs 1 mins |
| 27 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 711,633 | 124,103 | 5.73 | 4 hrs 11 mins |
| 28 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 631,011 | 112,503 | 5.61 | 4 hrs 17 mins |
| 29 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 615,593 | 119,080 | 5.17 | 4 hrs 39 mins |
| 30 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 503,331 | 114,521 | 4.40 | 5 hrs 28 mins |
| 31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 487,744 | 107,220 | 4.55 | 5 hrs 17 mins |
| 32 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 271,915 | 80,615 | 3.37 | 7 hrs 7 mins |
| 33 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 256,061 | 88,493 | 2.89 | 8 hrs 18 mins |
| 34 | Quadro M4000 GM204GL [Quadro M4000] |
Nvidia | GM204GL | 209,767 | 90,117 | 2.33 | 10 hrs 19 mins |
| 35 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 203,707 | 82,448 | 2.47 | 9 hrs 43 mins |
| 36 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 71,219 | 58,049 | 1.23 | 19 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:26:20|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|