RESEARCH: CANCER
FOLDING PROJECT #14934 PROFILE
PROJECT TEAM
Manager(s): Prateek BansalInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 105,048Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at Class F receptors, which help control how cells grow and change. When these proteins are overactive, they can lead to cancers like Basal Cell Carcinoma and Medulloblastoma. By using computer simulations, scientists hope to figure out how these proteins work and learn more about how these cancers develop.
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
A group of cell surface receptors involved in signaling pathways.
Class F Receptors are a family of proteins found on the surface of cells. They act as receptors, receiving signals from other molecules and triggering cellular responses. These receptors play a crucial role in various biological processes, including cell growth, differentiation, and survival. Disruptions in Class F receptor signaling can contribute to the development of certain cancers.
Cell Differentiation
The process by which a cell becomes specialized for a specific function.
Cell differentiation is the process by which unspecialized cells develop into specialized cells with specific functions. During development, stem cells undergo differentiation to give rise to all the diverse cell types in an organism. This process involves changes in gene expression and cellular structure, ultimately leading to the formation of tissues and organs.
Basal Cell Carcinoma
A type of skin cancer that originates in the basal cells.
Basal cell carcinoma is the most common type of skin cancer. It develops from the basal cells, which are found in the deepest layer of the epidermis (outermost layer of skin). Basal cell carcinomas typically appear as pearly or waxy bumps, often with visible blood vessels.
Medulloblastoma
A type of malignant brain tumor that arises from the cerebellum.
Medulloblastoma is a highly aggressive brain tumor that originates in the cerebellum, a part of the brain responsible for balance and coordination. It primarily affects children and adolescents. Medulloblastomas can grow rapidly and spread to other parts of the body.
Simulations
Computer-based models that mimic real-world processes.
Simulations are computer-generated models that attempt to replicate complex systems or processes. In drug discovery, simulations are used to predict how drugs will interact with target proteins and to design new molecules with desired properties.
Pathogenesis
The origin and development of a disease.
Pathogenesis refers to the biological processes that underlie the development and progression of a disease. Understanding pathogenesis is crucial for identifying potential therapeutic targets and developing effective treatments.
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 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,275,388 | 140,227 | 51.88 | 0 hrs 28 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,842,668 | 137,742 | 49.68 | 0 hrs 29 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,993,004 | 130,615 | 45.88 | 0 hrs 31 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,741,990 | 132,890 | 43.21 | 0 hrs 33 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,765,157 | 124,314 | 38.33 | 0 hrs 38 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,752,685 | 124,538 | 38.16 | 0 hrs 38 mins |
| 7 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,708,407 | 123,916 | 38.00 | 0 hrs 38 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,532,612 | 122,231 | 37.08 | 0 hrs 39 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,167,900 | 119,349 | 34.92 | 0 hrs 41 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,877,430 | 115,212 | 33.65 | 0 hrs 43 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,351,951 | 110,605 | 30.31 | 0 hrs 48 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 3,090,307 | 109,052 | 28.34 | 0 hrs 51 mins |
| 13 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 2,838,321 | 105,123 | 27.00 | 0 hrs 53 mins |
| 14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,766,234 | 98,506 | 28.08 | 0 hrs 51 mins |
| 15 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,559,554 | 100,723 | 25.41 | 0 hrs 57 mins |
| 16 | GeForce RTX 2070 SUPER Mobile / Max-Q TU104M [GeForce RTX 2070 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 2,511,253 | 100,248 | 25.05 | 0 hrs 57 mins |
| 17 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,356,869 | 99,113 | 23.78 | 1 hrs 1 mins |
| 18 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,309,953 | 98,136 | 23.54 | 1 hrs 1 mins |
| 19 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 2,174,738 | 96,458 | 22.55 | 1 hrs 4 mins |
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|
|||||||
| 20 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,109,715 | 94,671 | 22.28 | 1 hrs 5 mins |
| 21 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,982,242 | 93,531 | 21.19 | 1 hrs 8 mins |
| 22 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,612,186 | 86,280 | 18.69 | 1 hrs 17 mins |
| 23 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,462,083 | 85,027 | 17.20 | 1 hrs 24 mins |
| 24 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,457,919 | 68,146 | 21.39 | 1 hrs 7 mins |
| 25 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,340,166 | 82,292 | 16.29 | 1 hrs 28 mins |
| 26 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,335,409 | 81,917 | 16.30 | 1 hrs 28 mins |
| 27 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,334,871 | 81,884 | 16.30 | 1 hrs 28 mins |
| 28 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,222,312 | 70,736 | 17.28 | 1 hrs 23 mins |
| 29 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,203,529 | 75,705 | 15.90 | 1 hrs 31 mins |
| 30 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,128,463 | 77,520 | 14.56 | 1 hrs 39 mins |
| 31 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,064,413 | 73,505 | 14.48 | 1 hrs 39 mins |
| 32 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 647,545 | 64,426 | 10.05 | 2 hrs 23 mins |
| 33 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 607,163 | 63,591 | 9.55 | 2 hrs 31 mins |
| 34 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 588,659 | 61,862 | 9.52 | 2 hrs 31 mins |
| 35 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 579,625 | 61,855 | 9.37 | 2 hrs 34 mins |
| 36 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 576,562 | 61,783 | 9.33 | 2 hrs 34 mins |
| 37 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 486,480 | 58,387 | 8.33 | 2 hrs 53 mins |
| 38 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 328,709 | 51,453 | 6.39 | 3 hrs 45 mins |
| 39 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 201,656 | 43,563 | 4.63 | 5 hrs 11 mins |
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| 40 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 143,700 | 39,026 | 3.68 | 6 hrs 31 mins |
| 41 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 128,798 | 37,554 | 3.43 | 6 hrs 60 mins |
| 42 | GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634 |
Nvidia | GK104 | 95,452 | 33,585 | 2.84 | 8 hrs 27 mins |
| 43 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 94,401 | 33,811 | 2.79 | 8 hrs 36 mins |
| 44 | GeForce GTX 660 GK106 [GeForce GTX 660] |
Nvidia | GK106 | 66,982 | 30,269 | 2.21 | 10 hrs 51 mins |
| 45 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 52,655 | 16,800 | 3.13 | 7 hrs 39 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 |
|---|