RESEARCH: CANNABINOID
FOLDING PROJECT #12709 PROFILE
PROJECT TEAM
Manager(s): Michael ChenInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 149,254Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project relates to how cannabinoid receptors work in our bodies. These receptors are involved in things like pain, appetite, and learning. Scientists use computer models to study these receptors and see how different drugs affect them. This can help us develop new treatments for addiction and other conditions.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Cannabinoid G Protein Coupled Receptors Improper signaling through Cannabinoid receptors is linked to drug addiction, pain perception, appetite regulation and learning.
Simulation of these proteins with different small molecules helps us to understand their function and the selectivity/efficacy of these potential drugs to the nervous system.
RELATED TERMS GLOSSARY AI BETA
Cannabinoid
A class of chemical compounds that interact with the endocannabinoid system.
Cannabinoids are molecules that bind to specific receptors in the body, primarily found in the brain and nervous system. They play a role in various physiological processes, including pain perception, mood regulation, appetite control, and memory. Cannabinoids can be naturally produced by the body (endocannabinoids) or derived from plants like cannabis (phytocannabinoids). Medical marijuana utilizes cannabinoids to treat conditions like chronic pain, nausea, and anxiety.
Receptors
Proteins on the surface of cells that bind to specific molecules (ligands) and trigger a cellular response.
Receptors are specialized proteins found on the surfaces of cells. They act like locks, waiting for specific molecules (ligands) to fit into them like keys. When a ligand binds to a receptor, it triggers a series of events inside the cell, ultimately leading to a change in cellular activity. Receptors play crucial roles in many biological processes, including communication between cells, sensory perception, and drug action.
G Protein Coupled Receptors
GPCRs are a large family of membrane receptors that activate intracellular signaling pathways via G proteins.
G protein-coupled receptors (GPCRs) are a type of receptor found on the surface of cells. When a molecule binds to a GPCR, it triggers a cascade of events inside the cell by activating a protein called a G protein. This activation leads to various cellular responses, such as changes in gene expression, metabolism, and cell behavior. GPCRs are involved in many important physiological processes, including sensory perception, hormone signaling, and immune responses.
Drug Addiction
A chronic, relapsing disorder characterized by compulsive drug seeking and use despite harmful consequences.
Drug addiction is a serious mental health condition that involves an intense craving for and compulsive use of drugs despite negative consequences. It affects the brain's reward system, leading to changes in behavior, mood, and cognition. Factors contributing to addiction include genetics, environment, and individual susceptibility.
Pain Perception
The process of sensing and interpreting pain stimuli.
Pain perception is a complex process involving the detection of noxious stimuli by specialized sensory neurons (nociceptors), transmission of pain signals to the spinal cord and brain, and the interpretation of these signals as painful sensations. This involves both physical and emotional components, influenced by factors like genetics, past experiences, and cultural beliefs.
Appetite Regulation
The complex physiological processes that control food intake and energy expenditure.
Appetite regulation involves a intricate interplay of hormones, neurotransmitters, and behavioral factors that influence our desire to eat and how much we consume. This system helps maintain energy balance and ensure adequate nutrient intake for survival. Disruptions in appetite regulation can lead to conditions like obesity or anorexia nervosa.
Learning
The process of acquiring new knowledge or skills through experience and instruction.
Learning is a fundamental process that allows us to adapt to our environment and acquire knowledge. It involves changes in the structure and function of the brain, influenced by experiences and interactions with the world. Different types of learning include classical conditioning, operant conditioning, and observational learning.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:33:58|
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 5090 GB202 [GeForce RTX 5090] |
Nvidia | GB202 | 39,825,846 | 106,500 | 373.95 | 0 hrs 4 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 27,056,055 | 619,431 | 43.68 | 0 hrs 33 mins |
| 3 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 17,284,999 | 232,879 | 74.22 | 0 hrs 19 mins |
| 4 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 16,095,832 | 199,674 | 80.61 | 0 hrs 18 mins |
| 5 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 15,992,663 | 106,500 | 150.17 | 0 hrs 10 mins |
| 6 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 15,957,696 | 589,978 | 27.05 | 0 hrs 53 mins |
| 7 | GeForce RTX 5090 Max-Q / Mobile GB203M / GN22 [GeForce RTX 5090 Max-Q / Mobile] |
Nvidia | GB203M / GN22 | 15,574,316 | 106,500 | 146.24 | 0 hrs 10 mins |
| 8 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 11,885,538 | 582,430 | 20.41 | 1 hrs 11 mins |
| 9 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 11,491,340 | 200,447 | 57.33 | 0 hrs 25 mins |
| 10 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 11,095,404 | 570,181 | 19.46 | 1 hrs 14 mins |
| 11 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 10,794,203 | 563,697 | 19.15 | 1 hrs 15 mins |
| 12 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 10,353,842 | 106,500 | 97.22 | 0 hrs 15 mins |
| 13 | GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU] |
Nvidia | AD103M / GN21-X11 | 8,925,782 | 106,500 | 83.81 | 0 hrs 17 mins |
| 14 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 7,820,288 | 339,431 | 23.04 | 1 hrs 3 mins |
| 15 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 6,694,765 | 106,500 | 62.86 | 0 hrs 23 mins |
| 16 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 6,391,127 | 106,500 | 60.01 | 0 hrs 24 mins |
| 17 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 6,261,810 | 106,500 | 58.80 | 0 hrs 24 mins |
| 18 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,974,874 | 106,500 | 56.10 | 0 hrs 26 mins |
| 19 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 5,591,268 | 445,278 | 12.56 | 1 hrs 55 mins |
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|
|||||||
| 20 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,472,006 | 453,953 | 12.05 | 1 hrs 59 mins |
| 21 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 5,155,883 | 185,781 | 27.75 | 0 hrs 52 mins |
| 22 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,998,170 | 258,657 | 19.32 | 1 hrs 15 mins |
| 23 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 4,118,143 | 411,528 | 10.01 | 2 hrs 24 mins |
| 24 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,889,693 | 148,122 | 26.26 | 0 hrs 55 mins |
| 25 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 3,721,955 | 106,500 | 34.95 | 0 hrs 41 mins |
| 26 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,294,285 | 106,500 | 30.93 | 0 hrs 47 mins |
| 27 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super] |
Nvidia | TU106 | 3,223,617 | 106,500 | 30.27 | 0 hrs 48 mins |
| 28 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 3,132,508 | 373,232 | 8.39 | 2 hrs 52 mins |
| 29 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,993,569 | 106,500 | 28.11 | 0 hrs 51 mins |
| 30 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,770,130 | 106,500 | 26.01 | 0 hrs 55 mins |
| 31 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 2,737,835 | 355,962 | 7.69 | 3 hrs 7 mins |
| 32 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,617,693 | 106,500 | 24.58 | 0 hrs 59 mins |
| 33 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,499,846 | 218,595 | 11.44 | 2 hrs 6 mins |
| 34 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,476,343 | 187,147 | 13.23 | 1 hrs 49 mins |
| 35 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,370,748 | 345,750 | 6.86 | 3 hrs 30 mins |
| 36 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,654,089 | 304,222 | 5.44 | 4 hrs 25 mins |
| 37 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,646,911 | 106,500 | 15.46 | 1 hrs 33 mins |
| 38 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 1,500,008 | 106,500 | 14.08 | 1 hrs 42 mins |
| 39 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,462,682 | 106,500 | 13.73 | 1 hrs 45 mins |
|
|
|||||||
| 40 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,236,000 | 106,500 | 11.61 | 2 hrs 4 mins |
| 41 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,199,478 | 272,669 | 4.40 | 5 hrs 27 mins |
| 42 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,117,519 | 106,500 | 10.49 | 2 hrs 17 mins |
| 43 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 719,632 | 250,887 | 2.87 | 8 hrs 22 mins |
| 44 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 592,234 | 106,500 | 5.56 | 4 hrs 19 mins |
| 45 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 472,009 | 199,468 | 2.37 | 10 hrs 9 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:33:58|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|