RESEARCH: CANNABINOID
FOLDING PROJECT #19010 PROFILE
PROJECT TEAM
Manager(s): Soumajit DuttaInstitution: University of Illinois Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 89,957Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how different types of 'weed' affect the body. Scientists are comparing how two different types of cannabinoids (the stuff that makes weed work) interact with our brain cells. One type is like regular marijuana, while the other is a newer synthetic version. Understanding how these cannabinoids work can help us develop better medicines and warn about the dangers of harmful synthetic versions.
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
Receptors that bind cannabinoids and play a role in pain perception, appetite, mood, and other physiological processes.
Cannabinoid receptors are specialized proteins found throughout the body. They interact with chemicals called cannabinoids, which can be produced naturally by the body (endocannabinoids) or obtained from sources like cannabis. When activated, these receptors influence various functions, including pain sensation, appetite regulation, mood, and sleep.
Endocannabinoid Signaling System
A complex network of receptors, enzymes, and neurotransmitters that plays a crucial role in maintaining homeostasis.
The endocannabinoid system is a vital communication network within the body. It uses specialized chemicals called endocannabinoids to send signals between cells and organs. This system helps regulate numerous functions, including mood, appetite, sleep, pain perception, and immune responses.
Synthetic Cannabinoids
Man-made compounds that mimic the effects of naturally occurring cannabinoids.
Synthetic cannabinoids are artificial chemicals designed to produce similar effects to those found in cannabis. These substances often target the same receptors as natural cannabinoids but may have different potencies and side effects.
Novel Psychoactive Substance (NPS)
New psychoactive substances are synthetic drugs designed to mimic the effects of controlled substances.
Novel psychoactive substances (NPS) are a growing concern due to their unpredictable effects and potential for harm. These substances often bypass legal restrictions by slightly altering the chemical structure of existing controlled drugs.
MDMB-Fubinaca
A synthetic cannabinoid with potent effects.
MDMB-Fubinaca is a synthetic cannabinoid known for its strong psychoactive effects. It has been associated with adverse health outcomes, including agitation, hallucinations, and seizures.
HU-210
A classical cannabinoid agonist that binds to cannabinoid receptors.
HU-210 is a synthetic cannabinoid compound with known pharmacological activity. It has been studied for its potential therapeutic applications in conditions such as chronic pain and multiple sclerosis.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:26:22|
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 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 15,033,611 | 342,837 | 43.85 | 0 hrs 33 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 12,889,510 | 332,935 | 38.71 | 0 hrs 37 mins |
| 3 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 8,898,958 | 289,443 | 30.75 | 0 hrs 47 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,118,404 | 268,170 | 26.54 | 0 hrs 54 mins |
| 5 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,465,724 | 259,310 | 24.93 | 0 hrs 58 mins |
| 6 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,929,262 | 249,557 | 23.76 | 1 hrs 1 mins |
| 7 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 5,830,272 | 255,018 | 22.86 | 1 hrs 3 mins |
| 8 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,008,504 | 242,257 | 20.67 | 1 hrs 10 mins |
| 9 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,722,427 | 234,324 | 20.15 | 1 hrs 11 mins |
| 10 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,338,238 | 228,045 | 19.02 | 1 hrs 16 mins |
| 11 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,258,421 | 224,987 | 18.93 | 1 hrs 16 mins |
| 12 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,783,241 | 216,952 | 17.44 | 1 hrs 23 mins |
| 13 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,461,231 | 191,352 | 18.09 | 1 hrs 20 mins |
| 14 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 3,113,631 | 205,042 | 15.19 | 1 hrs 35 mins |
| 15 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 3,017,374 | 194,789 | 15.49 | 1 hrs 33 mins |
| 16 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,973,316 | 200,479 | 14.83 | 1 hrs 37 mins |
| 17 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,894,011 | 199,085 | 14.54 | 1 hrs 39 mins |
| 18 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,788,340 | 197,053 | 14.15 | 1 hrs 42 mins |
| 19 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,358,880 | 179,781 | 13.12 | 1 hrs 50 mins |
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|
|||||||
| 20 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,291,895 | 183,654 | 12.48 | 1 hrs 55 mins |
| 21 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,065,730 | 178,408 | 11.58 | 2 hrs 4 mins |
| 22 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,816,971 | 171,438 | 10.60 | 2 hrs 16 mins |
| 23 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,707,689 | 167,416 | 10.20 | 2 hrs 21 mins |
| 24 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,700,979 | 161,344 | 10.54 | 2 hrs 17 mins |
| 25 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,633,081 | 164,724 | 9.91 | 2 hrs 25 mins |
| 26 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,320,085 | 153,402 | 8.61 | 2 hrs 47 mins |
| 27 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,238,654 | 149,305 | 8.30 | 2 hrs 54 mins |
| 28 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,142,100 | 145,448 | 7.85 | 3 hrs 3 mins |
| 29 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,117,506 | 142,953 | 7.82 | 3 hrs 4 mins |
| 30 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,038,128 | 139,438 | 7.45 | 3 hrs 13 mins |
| 31 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 984,230 | 115,672 | 8.51 | 2 hrs 49 mins |
| 32 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 864,160 | 133,592 | 6.47 | 3 hrs 43 mins |
| 33 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 785,032 | 129,855 | 6.05 | 3 hrs 58 mins |
| 34 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 726,319 | 126,078 | 5.76 | 4 hrs 10 mins |
| 35 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 584,095 | 116,943 | 4.99 | 4 hrs 48 mins |
| 36 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 543,659 | 114,190 | 4.76 | 5 hrs 2 mins |
| 37 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 534,893 | 111,303 | 4.81 | 4 hrs 60 mins |
| 38 | Quadro P2200 GP106GL [Quadro P2200] |
Nvidia | GP106GL | 287,779 | 91,373 | 3.15 | 7 hrs 37 mins |
| 39 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 260,262 | 89,740 | 2.90 | 8 hrs 17 mins |
|
|
|||||||
| 40 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 185,948 | 85,944 | 2.16 | 11 hrs 6 mins |
| 41 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 113,492 | 68,027 | 1.67 | 14 hrs 23 mins |
| 42 | Quadro M2000 GM206GL [Quadro M2000] |
Nvidia | GM206GL | 81,187 | 63,691 | 1.27 | 18 hrs 50 mins |
| 43 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 80,926 | 60,420 | 1.34 | 17 hrs 55 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:26:22|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|