RESEARCH: CANCER
FOLDING PROJECT #18107 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 19,714Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies KRas, a protein that controls cell growth. It can become mutated in cancer, causing uncontrolled cell division. Scientists are using simulations to understand how KRas works with inhibitors (drugs) so they can design better cancer treatments. All data will be publicly available.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
We are simulating publicly available protein and small molecule structures of the currently very hot cancer target KRas, see https://www.fiercepharma.com/pharma/amgen-s-lumakras-becomes-first-fda-approved-kras-inhibitor-for-lung-cancer-patients for recent developments.
Folding@home has previously looked at this protein (in project 10490), and the following part of the description is copied from there: This project is "studying a small protein called KRAS, which forms a key link in growth signaling and cancer.
This gene is something like a molecular switch with a timer.
When it is bound to a molecule called GDP, it is off, and does not signal that the cell should grow.
However, other proteins can cause it to swap its GDP for a GTP, turning KRAS on.
In the on state, it signals that the cell should grow and divide.
Normally, after some time, KRAS, with the aid of some partners, will chemically convert its GTP to GDP and return to its inactive state. In many cancers, this protein becomes mutated, and cannot return to its off state.
The result? The cells continue to divide without limit.
What’s worse, cancers with this protein mutated tend to have much poorer prognoses.
As a result, scientists have been trying to target this protein for decades." We are investigating the dynamic behavior of KRas with these publicly disclosed inhibitors so that we can apply this knowledge to our own drug design.
At the same time, we are further testing the adaptive sampling methodology.
All data is being made publicly available, and insights from methodology developments will be shared.
This is a project run by Roivant Sciences (formerly Silicon Therapeutics) as was officially announced in this press release: https://foldingathome.org/2021/04/20/maximizing-the-impact-of-foldinghome-by-engaging-industry-collaborators/.
RELATED TERMS GLOSSARY AI BETA
KRas
A protein that plays a role in cell growth and division.
KRas is a protein that acts like a switch controlling cell growth. When it's active, cells divide. In many cancers, KRas malfunctions, constantly sending signals for cell division, leading to uncontrolled tumor growth. Scientists are developing drugs to target KRas and stop its harmful activity.
GDP
Guanosine diphosphate
GDP is a molecule that binds to KRas, keeping it in an inactive state. Think of it like a 'off' switch for cell growth.
GTP
Guanosine triphosphate
GTP is a molecule that binds to KRas, activating it and triggering cell growth. It's like the 'on' switch for cell division.
Inhibitors
Substances that block the action of a protein or enzyme.
Inhibitors are molecules designed to prevent KRas from working properly. Scientists hope they can use inhibitors to stop cancer cells from dividing.
Prognosis
The likely course or outcome of a disease.
Prognosis refers to the expected outcome for a patient with a particular illness. A poor prognosis means the disease is likely to worsen over time.
Folding@home
A distributed computing project that uses volunteer computer power to simulate protein folding.
Folding@home is a global effort where people donate their computer's processing power to help scientists understand how proteins fold and function. This research can lead to new treatments for diseases like cancer.
Roivant Sciences
A biotechnology company focused on developing and commercializing new medicines.
Roivant Sciences is a pharmaceutical company that researches and develops new drugs. They are working with Folding@home to study KRas and develop potential cancer treatments.
Silicon Therapeutics
A former name for Roivant Sciences.
Silicon Therapeutics was the original name for Roivant Sciences before it rebranded.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:20|
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 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 3,569,895 | 227,591 | 15.69 | 1 hrs 32 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 2,834,061 | 121,243 | 23.38 | 1 hrs 2 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 2,735,741 | 122,359 | 22.36 | 1 hrs 4 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 2,383,248 | 117,599 | 20.27 | 1 hrs 11 mins |
| 5 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 2,368,243 | 117,086 | 20.23 | 1 hrs 11 mins |
| 6 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 2,329,664 | 116,786 | 19.95 | 1 hrs 12 mins |
| 7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 2,317,037 | 116,160 | 19.95 | 1 hrs 12 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 2,202,566 | 113,696 | 19.37 | 1 hrs 14 mins |
| 9 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,117,761 | 113,095 | 18.73 | 1 hrs 17 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 1,896,035 | 109,278 | 17.35 | 1 hrs 23 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,847,991 | 108,352 | 17.06 | 1 hrs 24 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 1,685,270 | 105,126 | 16.03 | 1 hrs 30 mins |
| 13 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 1,669,798 | 104,654 | 15.96 | 1 hrs 30 mins |
| 14 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,633,879 | 103,900 | 15.73 | 1 hrs 32 mins |
| 15 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 1,615,608 | 103,544 | 15.60 | 1 hrs 32 mins |
| 16 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,563,144 | 102,491 | 15.25 | 1 hrs 34 mins |
| 17 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 1,506,547 | 100,723 | 14.96 | 1 hrs 36 mins |
| 18 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 1,454,450 | 100,068 | 14.53 | 1 hrs 39 mins |
| 19 | TITAN X GP102 [TITAN X] 6144 |
Nvidia | GP102 | 1,443,058 | 99,368 | 14.52 | 1 hrs 39 mins |
|
|
|||||||
| 20 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,400,610 | 98,888 | 14.16 | 1 hrs 42 mins |
| 21 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,339,662 | 97,174 | 13.79 | 1 hrs 44 mins |
| 22 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,307,351 | 95,383 | 13.71 | 1 hrs 45 mins |
| 23 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,278,273 | 94,477 | 13.53 | 1 hrs 46 mins |
| 24 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,195,573 | 93,669 | 12.76 | 1 hrs 53 mins |
| 25 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,138,482 | 91,976 | 12.38 | 1 hrs 56 mins |
| 26 | Quadro P5000 GP104GL [Quadro P5000] |
Nvidia | GP104GL | 1,028,479 | 82,135 | 12.52 | 1 hrs 55 mins |
| 27 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 992,296 | 87,926 | 11.29 | 2 hrs 8 mins |
| 28 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 973,661 | 71,463 | 13.62 | 1 hrs 46 mins |
| 29 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 969,299 | 87,222 | 11.11 | 2 hrs 10 mins |
| 30 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 924,486 | 85,666 | 10.79 | 2 hrs 13 mins |
| 31 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 902,504 | 85,128 | 10.60 | 2 hrs 16 mins |
| 32 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 742,976 | 63,840 | 11.64 | 2 hrs 4 mins |
| 33 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 713,685 | 79,428 | 8.99 | 2 hrs 40 mins |
| 34 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 648,881 | 76,477 | 8.48 | 2 hrs 50 mins |
| 35 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 633,627 | 75,683 | 8.37 | 2 hrs 52 mins |
| 36 | GeForce GTX 1650 TU117 [GeForce GTX 1650] 3091 |
Nvidia | TU117 | 516,669 | 71,206 | 7.26 | 3 hrs 18 mins |
| 37 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 453,265 | 67,737 | 6.69 | 3 hrs 35 mins |
| 38 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 430,781 | 66,548 | 6.47 | 3 hrs 42 mins |
| 39 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 420,615 | 64,213 | 6.55 | 3 hrs 40 mins |
|
|
|||||||
| 40 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 417,424 | 60,217 | 6.93 | 3 hrs 28 mins |
| 41 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 395,819 | 64,660 | 6.12 | 3 hrs 55 mins |
| 42 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 255,328 | 56,126 | 4.55 | 5 hrs 17 mins |
| 43 | GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile] |
Nvidia | GP107M | 192,775 | 50,998 | 3.78 | 6 hrs 21 mins |
| 44 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 192,693 | 51,272 | 3.76 | 6 hrs 23 mins |
| 45 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 116,018 | 43,017 | 2.70 | 8 hrs 54 mins |
| 46 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 86,161 | 32,986 | 2.61 | 9 hrs 11 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:20|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|