RESEARCH: CANCER
FOLDING PROJECT #17762 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 103,391Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how special proteins use energy from ions to move other molecules across cell membranes. These proteins are found everywhere and help treat diseases like cancer and diabetes. Simulations will help us understand how these proteins work across different types of cells.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17745-17750 Molecular basis of secondary active transporters. Secondary active membrane transporters are proteins that utilize ions to transport an assortment of molecules across cell membranes.
These proteins are found in all domains in life and surprisingly, despite vastly different structures, operate under the same mechanism by using an ion gradient to assist in small molecule transport.
Furthermore, many of these secondary active transporters are drug targets to treat diseases like cancer, diabetes, and neurological disorders.
The simulations in this project will allow us to understand a universal role of ion-coupling across different families of proteins.
RELATED TERMS GLOSSARY AI BETA
Secondary active transporters
Proteins that use ion gradients to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They use the energy stored in an ion gradient to move other molecules across cell membranes. This process is crucial for various cellular functions, including nutrient uptake, waste removal, and signal transduction. Because of their importance in diverse biological processes, secondary active transporters are often targeted by drugs to treat diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient refers to the unequal distribution of ions (charged atoms) across a cell membrane. This difference in concentration creates an electrochemical potential that can be used to drive various cellular processes, including the movement of molecules across the membrane via secondary active transporters.
Drug targets
Molecules or biological processes that are targeted by drugs to treat diseases.
Drug targets are specific molecules or pathways within the body that are involved in disease processes. By inhibiting or activating these targets, drugs can effectively treat various conditions. Secondary active transporters often serve as drug targets due to their crucial roles in cellular functions and their involvement in many diseases.
Cancer
A group of diseases characterized by uncontrolled cell growth.
Cancer is a broad term encompassing various diseases characterized by the abnormal and uncontrollable proliferation of cells. This uncontrolled growth can lead to the formation of tumors and the spread of cancer cells to other parts of the body (metastasis).
Diabetes
A metabolic disorder characterized by high blood sugar levels.
Diabetes is a chronic condition affecting how the body regulates blood sugar (glucose). In type 1 diabetes, the immune system destroys insulin-producing cells in the pancreas. In type 2 diabetes, the body becomes resistant to insulin or doesn't produce enough. This leads to elevated blood sugar levels, which can damage various organs over time.
Neurological disorders
Conditions affecting the nervous system.
Neurological disorders encompass a wide range of conditions affecting the brain, spinal cord, and nerves. These can include Alzheimer's disease, Parkinson's disease, stroke, epilepsy, multiple sclerosis, and various others.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:56|
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 | 6,829,141 | 172,083 | 39.69 | 0 hrs 36 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,807,598 | 166,122 | 40.98 | 0 hrs 35 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,547,778 | 169,284 | 38.68 | 0 hrs 37 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,546,817 | 160,498 | 34.56 | 0 hrs 42 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,472,251 | 149,384 | 29.94 | 0 hrs 48 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,211,305 | 146,411 | 28.76 | 0 hrs 50 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,686,335 | 140,798 | 26.18 | 0 hrs 55 mins |
| 8 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,122,845 | 132,613 | 23.55 | 1 hrs 1 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,666,212 | 126,432 | 21.09 | 1 hrs 8 mins |
| 10 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,549,978 | 124,921 | 20.41 | 1 hrs 11 mins |
| 11 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,492,666 | 124,056 | 20.09 | 1 hrs 12 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,108,015 | 116,286 | 18.13 | 1 hrs 19 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,786,300 | 108,620 | 16.45 | 1 hrs 28 mins |
| 14 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,780,986 | 109,926 | 16.20 | 1 hrs 29 mins |
| 15 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,567,089 | 106,093 | 14.77 | 1 hrs 37 mins |
| 16 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,473,029 | 103,998 | 14.16 | 1 hrs 42 mins |
| 17 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,400,062 | 101,677 | 13.77 | 1 hrs 45 mins |
| 18 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,332,079 | 97,715 | 13.63 | 1 hrs 46 mins |
| 19 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,192,085 | 96,921 | 12.30 | 1 hrs 57 mins |
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| 20 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,177,925 | 96,452 | 12.21 | 1 hrs 58 mins |
| 21 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,175,611 | 93,885 | 12.52 | 1 hrs 55 mins |
| 22 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,171,392 | 95,865 | 12.22 | 1 hrs 58 mins |
| 23 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 967,989 | 90,213 | 10.73 | 2 hrs 14 mins |
| 24 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 778,500 | 83,713 | 9.30 | 2 hrs 35 mins |
| 25 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 694,806 | 77,620 | 8.95 | 2 hrs 41 mins |
| 26 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 583,355 | 75,945 | 7.68 | 3 hrs 7 mins |
| 27 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 566,286 | 74,928 | 7.56 | 3 hrs 11 mins |
| 28 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 541,613 | 74,175 | 7.30 | 3 hrs 17 mins |
| 29 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 523,176 | 73,686 | 7.10 | 3 hrs 23 mins |
| 30 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 367,084 | 65,677 | 5.59 | 4 hrs 18 mins |
| 31 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 320,047 | 62,598 | 5.11 | 4 hrs 42 mins |
| 32 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 295,789 | 61,089 | 4.84 | 4 hrs 57 mins |
| 33 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 275,699 | 58,970 | 4.68 | 5 hrs 8 mins |
| 34 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 226,823 | 55,392 | 4.09 | 5 hrs 52 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:56|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|