PROJECT #19012 RESEARCH FOR CANNABINOID
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

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..

PROJECT INFO

Manager(s): Soumajit Dutta

Institution: University of Illinois Urbana-Champaign

Project URL: shuklagroup.org/

PROJECT WORK UNIT SUMMARY

Atoms: 90,246

Core: 0x22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Sunday, 04 December 2022 18:13:18

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 2 hrs 37 mins
11 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,287,232 183,428 12.47 2 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 3 hrs 35 mins
16 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,416,935 161,227 8.79 3 hrs 44 mins
17 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,416,102 152,839 9.27 3 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 3 hrs 47 mins
20 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 1,308,351 150,521 8.69 3 hrs 46 mins
21 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,254,696 147,773 8.49 3 hrs 50 mins
22 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,172,940 140,447 8.35 3 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 4 hrs 36 mins
25 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 824,556 126,842 6.50 4 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 5 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 10 hrs 43 mins
36 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 71,219 58,049 1.23 20 hrs 34 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 04 December 2022 18:13:18

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make