Folding project #12709, CANNABINOID research

Research section Research projects and references

Project #12709 overview

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.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Michael Chen
Institution: University of Illinois Urbana-Champaign

Work unit

Atoms: 149,254
Core: 0x27
Status: Public

Source material

Official 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.

Reading reference

Related Terms Glossary AI Beta

Automated high-level definitions; simplified and may not be fully accurate.

Cannabinoid

A class of chemical compounds that interact with the endocannabinoid system.

Scientific: BiotechnologyPharmacology / Neuropharmacology

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.

Scientific: BiotechnologyPharmacology / Neuropharmacology

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.

Scientific: BiotechnologyPharmacology / Neuropharmacology

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.

Technical: PharmaceuticalsMedicine / Psychiatry

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.

Scientific: PharmaceuticalsMedicine / Neurology

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.

Scientific: PharmaceuticalsMedicine / Endocrinology

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.

Scientific: PharmaceuticalsMedicine / Neuroscience

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.

GPU PPD Averages

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

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

Research: CANNABINOID Folding Project #12709

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 12709

GPU PPD Averages

Research: CANNABINOID Folding Project #12709

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages