RESEARCH: CANCER
FOLDING PROJECT #14935 PROFILE
PROJECT TEAM
Manager(s): Prateek BansalInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 105,385Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at Class F Receptors, which control how cells develop. Overactive receptors can cause cancer like Basal Cell Carcinoma and Medulloblastoma. By using computer models, researchers want to figure out how these receptors work, helping us understand and treat these diseases better.
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
Receptors that play a role in cell differentiation.
Class F receptors are a type of protein found on the surface of cells. They receive signals from other molecules and help to control how cells grow and develop. Overactivation of these receptors has been linked to certain types of cancer, such as basal cell carcinoma and medulloblastoma.
Basal Cell Carcinoma
A common type of skin cancer that originates in the basal cells.
Basal cell carcinoma is the most common form of skin cancer. It develops from the basal cells, which are located in the deepest layer of the epidermis (outermost layer of skin). Basal cell carcinomas typically appear as pearly or waxy bumps and can grow slowly.
Medulloblastoma
A type of malignant brain tumor that arises in the cerebellum.
Medulloblastoma is a fast-growing brain tumor that occurs most often in children. It originates in the cerebellum, a part of the brain responsible for balance and coordination. Medulloblastoma can spread to other parts of the body.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:32:41|
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 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,251,972 | 139,965 | 51.81 | 0 hrs 28 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,969,150 | 134,068 | 51.98 | 0 hrs 28 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,664,416 | 133,331 | 49.98 | 0 hrs 29 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,827,244 | 129,417 | 45.03 | 0 hrs 32 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,637,443 | 120,492 | 38.49 | 0 hrs 37 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,258,136 | 116,851 | 36.44 | 0 hrs 40 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,961,025 | 114,338 | 34.64 | 0 hrs 42 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,811,691 | 109,134 | 34.93 | 0 hrs 41 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,757,657 | 113,078 | 33.23 | 0 hrs 43 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,331,796 | 107,975 | 30.86 | 0 hrs 47 mins |
| 11 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,480,383 | 97,607 | 25.41 | 0 hrs 57 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,420,675 | 97,296 | 24.88 | 0 hrs 58 mins |
| 13 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,363,877 | 89,453 | 26.43 | 0 hrs 54 mins |
| 14 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,269,840 | 95,171 | 23.85 | 1 hrs 0 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,134,381 | 93,346 | 22.87 | 1 hrs 3 mins |
| 16 | GeForce RTX 2070 SUPER Mobile / Max-Q TU104M [GeForce RTX 2070 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 2,121,486 | 84,404 | 25.13 | 0 hrs 57 mins |
| 17 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,022,654 | 91,024 | 22.22 | 1 hrs 5 mins |
| 18 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,921,047 | 90,401 | 21.25 | 1 hrs 8 mins |
| 19 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,848,409 | 88,869 | 20.80 | 1 hrs 9 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 | 1,637,992 | 85,308 | 19.20 | 1 hrs 15 mins |
| 21 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,441,844 | 82,318 | 17.52 | 1 hrs 22 mins |
| 22 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,409,852 | 81,035 | 17.40 | 1 hrs 23 mins |
| 23 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,406,563 | 81,357 | 17.29 | 1 hrs 23 mins |
| 24 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,306,136 | 78,073 | 16.73 | 1 hrs 26 mins |
| 25 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,289,979 | 78,453 | 16.44 | 1 hrs 28 mins |
| 26 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,220,980 | 77,447 | 15.77 | 1 hrs 31 mins |
| 27 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,013,432 | 73,139 | 13.86 | 1 hrs 44 mins |
| 28 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 913,106 | 65,915 | 13.85 | 1 hrs 44 mins |
| 29 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 795,830 | 67,240 | 11.84 | 2 hrs 2 mins |
| 30 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 684,446 | 64,382 | 10.63 | 2 hrs 15 mins |
| 31 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 551,678 | 58,411 | 9.44 | 2 hrs 32 mins |
| 32 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 489,396 | 57,073 | 8.57 | 2 hrs 48 mins |
| 33 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 465,273 | 56,091 | 8.29 | 2 hrs 54 mins |
| 34 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 331,061 | 50,318 | 6.58 | 3 hrs 39 mins |
| 35 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 313,148 | 48,128 | 6.51 | 3 hrs 41 mins |
| 36 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 309,721 | 49,115 | 6.31 | 3 hrs 48 mins |
| 37 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 191,775 | 42,059 | 4.56 | 5 hrs 16 mins |
| 38 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 112,066 | 34,869 | 3.21 | 7 hrs 28 mins |
| 39 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 96,187 | 33,175 | 2.90 | 8 hrs 17 mins |
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| 40 | GeForce GTX 660 GK106 [GeForce GTX 660] |
Nvidia | GK106 | 67,493 | 29,736 | 2.27 | 10 hrs 34 mins |
| 41 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 50,766 | 26,888 | 1.89 | 12 hrs 43 mins |
| 42 | GeForce GT 730 GK208B [GeForce GT 730] 692.7 |
Nvidia | GK208B | 30,103 | 22,720 | 1.32 | 18 hrs 7 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:32:41|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|