RESEARCH: CANCER
FOLDING PROJECT #14933 PROFILE
PROJECT TEAM
Manager(s): Prateek BansalInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 105,522Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at Class F Receptors, proteins that help control how cells grow and change. Too much activity in these proteins can cause cancers like Basal Cell Carcinoma and Medulloblastoma. By using computer simulations, researchers hope to understand how these proteins work so they can better study and treat these diseases.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Class F Receptors Class F Receptors are involved in the control of cell differentiation.
Over-activation of these proteins have links to Basal Cell Carcinoma and Medulloblastoma.
Through Simulations we aim to understand the activation mechanisms of these proteins, giving us a way to probe into the pathogenesis of the disease.
RELATED TERMS GLOSSARY AI BETA
Class F Receptors
Membrane proteins that bind to specific ligands and initiate intracellular signaling cascades.
Class F receptors are a type of protein found on the surface of cells. They act as receptors, binding to specific molecules called ligands. This binding triggers a series of events inside the cell, known as signaling cascades, which ultimately regulate various cellular functions. These receptors play crucial roles in processes like cell growth, differentiation, and survival. Disruptions in their function can contribute to diseases such as cancer.
Basal Cell Carcinoma
A common type of skin cancer that arises from the basal cells in the epidermis.
Basal cell carcinoma is the most prevalent form of skin cancer. It develops from the basal cells, which are located in the deepest layer of the epidermis (the outermost layer of skin). These tumors typically appear as pearly or waxy bumps and often have a visible blood vessel network. Basal cell carcinomas grow slowly and rarely spread to other parts of the body.
Medulloblastoma
A type of malignant brain tumor that originates in the cerebellum.
Medulloblastoma is a highly aggressive form of childhood brain cancer. It arises from cells in the cerebellum, the part of the brain responsible for coordinating movement and balance. Medulloblastomas tend to grow rapidly and can spread to other parts of the nervous system.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:32:42|
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 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,008,299 | 135,217 | 51.83 | 0 hrs 28 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,691,256 | 135,120 | 49.52 | 0 hrs 29 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,889,361 | 128,621 | 45.79 | 0 hrs 31 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,585,713 | 128,338 | 43.52 | 0 hrs 33 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,676,418 | 120,278 | 38.88 | 0 hrs 37 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,637,973 | 120,455 | 38.50 | 0 hrs 37 mins |
| 7 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,423,720 | 118,999 | 37.17 | 0 hrs 39 mins |
| 8 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,407,662 | 116,791 | 37.74 | 0 hrs 38 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,044,320 | 115,116 | 35.13 | 0 hrs 41 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,889,330 | 113,700 | 34.21 | 0 hrs 42 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,179,202 | 106,269 | 29.92 | 0 hrs 48 mins |
| 12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,015,278 | 102,214 | 29.50 | 0 hrs 49 mins |
| 13 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,640,591 | 100,291 | 26.33 | 0 hrs 55 mins |
| 14 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,249,969 | 94,967 | 23.69 | 1 hrs 1 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,239,312 | 94,561 | 23.68 | 1 hrs 1 mins |
| 16 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,127,963 | 93,591 | 22.74 | 1 hrs 3 mins |
| 17 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,042,500 | 91,484 | 22.33 | 1 hrs 4 mins |
| 18 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 2,020,003 | 91,181 | 22.15 | 1 hrs 5 mins |
| 19 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,949,249 | 90,719 | 21.49 | 1 hrs 7 mins |
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|
|||||||
| 20 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,809,944 | 87,899 | 20.59 | 1 hrs 10 mins |
| 21 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,746,503 | 87,795 | 19.89 | 1 hrs 12 mins |
| 22 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,353,435 | 80,352 | 16.84 | 1 hrs 25 mins |
| 23 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,290,917 | 78,597 | 16.42 | 1 hrs 28 mins |
| 24 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,155,007 | 75,985 | 15.20 | 1 hrs 35 mins |
| 25 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,090,026 | 72,652 | 15.00 | 1 hrs 36 mins |
| 26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,050,794 | 73,933 | 14.21 | 1 hrs 41 mins |
| 27 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 905,083 | 69,782 | 12.97 | 1 hrs 51 mins |
| 28 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 857,080 | 68,447 | 12.52 | 1 hrs 55 mins |
| 29 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 801,305 | 67,149 | 11.93 | 2 hrs 1 mins |
| 30 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 681,387 | 63,825 | 10.68 | 2 hrs 15 mins |
| 31 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 657,519 | 40,941 | 16.06 | 1 hrs 30 mins |
| 32 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 642,927 | 61,202 | 10.50 | 2 hrs 17 mins |
| 33 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 590,804 | 61,369 | 9.63 | 2 hrs 30 mins |
| 34 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 588,988 | 61,078 | 9.64 | 2 hrs 29 mins |
| 35 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 559,104 | 60,181 | 9.29 | 2 hrs 35 mins |
| 36 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 557,340 | 59,520 | 9.36 | 2 hrs 34 mins |
| 37 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 540,077 | 59,293 | 9.11 | 2 hrs 38 mins |
| 38 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 287,726 | 47,813 | 6.02 | 3 hrs 59 mins |
| 39 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 213,734 | 42,672 | 5.01 | 4 hrs 47 mins |
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|||||||
| 40 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 197,572 | 42,672 | 4.63 | 5 hrs 11 mins |
| 41 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 164,668 | 39,037 | 4.22 | 5 hrs 41 mins |
| 42 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 131,405 | 36,751 | 3.58 | 6 hrs 43 mins |
| 43 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 128,996 | 36,612 | 3.52 | 6 hrs 49 mins |
| 44 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 91,937 | 32,667 | 2.81 | 8 hrs 32 mins |
| 45 | GeForce GT 730 GK208B [GeForce GT 730] 692.7 |
Nvidia | GK208B | 30,128 | 22,721 | 1.33 | 18 hrs 6 mins |
| 46 | GeForce GT 720 GK208 [GeForce GT 720] |
Nvidia | GK208 | 13,291 | 16,200 | 0.82 | 29 hrs 15 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:32:42|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|