RESEARCH: CANCER
FOLDING PROJECT #18039 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 130,000Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Scientists are using supercomputers to study how a protein called KRas works in cancer cells. KRas is like a switch that controls cell growth, and when it's broken, cancer can develop. This project aims to understand how drugs designed to block KRas work and could lead to new treatments for cancer.
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 at https://console.cloud.google.com/storage/browser/stxfah-bucket, 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/ All data is being made publicly available as soon as it is received at https://console.cloud.google.com/storage/browser/stxfah-bucket.
RELATED TERMS GLOSSARY AI BETA
KRas
Kirsten rat sarcoma viral oncogene homolog
KRas is a protein that plays a crucial role in cell growth and division. Mutations in the KRas gene are found in many types of cancer, leading to uncontrolled cell proliferation. Scientists are actively researching drugs to target KRas and treat these cancers.
GDP
Guanosine diphosphate
GDP is a molecule that binds to proteins like KRas, turning them 'off' and preventing cell growth. When GDP is replaced by GTP, the protein becomes active.
GTP
Guanosine triphosphate
GTP is a molecule that binds to proteins like KRas, turning them 'on' and stimulating cell growth. When GTP is converted back to GDP, the protein becomes inactive.
Inhibitors
Substances that block the activity of a specific protein or enzyme.
Inhibitors are molecules designed to prevent the action of KRas. By blocking KRas, researchers aim to stop cancer cell growth and spread.
Drug Design
The process of creating new medications.
Drug design involves identifying and developing molecules that can treat diseases. Researchers use various techniques to optimize drug candidates for efficacy, safety, and delivery.
Folding@home
A distributed computing project that harnesses the power of volunteers' computers to simulate protein folding and other biological processes.
Folding@home uses volunteer computing to perform complex simulations of proteins. This helps researchers understand how proteins fold and function, which is essential for drug discovery and other areas of biology.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:38|
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 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 8,055,501 | 890,937 | 9.04 | 2 hrs 39 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,548,497 | 816,872 | 8.02 | 2 hrs 60 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,112,431 | 806,583 | 7.58 | 3 hrs 10 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,997,828 | 789,538 | 7.60 | 3 hrs 10 mins |
| 5 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,239,842 | 763,444 | 6.86 | 3 hrs 30 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,498,200 | 737,260 | 6.10 | 3 hrs 56 mins |
| 7 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,375,579 | 728,557 | 6.01 | 3 hrs 60 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,993,959 | 701,382 | 5.69 | 4 hrs 13 mins |
| 9 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,820,018 | 697,974 | 5.47 | 4 hrs 23 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,713,119 | 674,287 | 5.51 | 4 hrs 21 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,521,353 | 666,690 | 5.28 | 4 hrs 33 mins |
| 12 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,504,892 | 672,490 | 5.21 | 4 hrs 36 mins |
| 13 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,788,929 | 618,762 | 4.51 | 5 hrs 19 mins |
| 14 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,718,648 | 626,019 | 4.34 | 5 hrs 32 mins |
| 15 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,415,123 | 586,868 | 4.12 | 5 hrs 50 mins |
| 16 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,271,649 | 581,597 | 3.91 | 6 hrs 9 mins |
| 17 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 2,178,399 | 571,883 | 3.81 | 6 hrs 18 mins |
| 18 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,080,789 | 582,752 | 3.57 | 6 hrs 43 mins |
| 19 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,987,168 | 554,856 | 3.58 | 6 hrs 42 mins |
|
|
|||||||
| 20 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,899,250 | 566,518 | 3.35 | 7 hrs 10 mins |
| 21 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,862,846 | 548,941 | 3.39 | 7 hrs 4 mins |
| 22 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,857,458 | 542,822 | 3.42 | 7 hrs 1 mins |
| 23 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,843,181 | 536,675 | 3.43 | 6 hrs 59 mins |
| 24 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,647,707 | 524,738 | 3.14 | 7 hrs 39 mins |
| 25 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,472,239 | 501,856 | 2.93 | 8 hrs 11 mins |
| 26 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,379,464 | 507,849 | 2.72 | 8 hrs 50 mins |
| 27 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,232,630 | 476,397 | 2.59 | 9 hrs 17 mins |
| 28 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,143,597 | 471,580 | 2.43 | 9 hrs 54 mins |
| 29 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,136,675 | 478,058 | 2.38 | 10 hrs 6 mins |
| 30 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,061,465 | 458,697 | 2.31 | 10 hrs 22 mins |
| 31 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,002,580 | 442,471 | 2.27 | 10 hrs 36 mins |
| 32 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 974,501 | 448,113 | 2.17 | 11 hrs 2 mins |
| 33 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 954,446 | 499,889 | 1.91 | 12 hrs 34 mins |
| 34 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 928,292 | 429,974 | 2.16 | 11 hrs 7 mins |
| 35 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 882,145 | 460,169 | 1.92 | 12 hrs 31 mins |
| 36 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 873,563 | 451,301 | 1.94 | 12 hrs 24 mins |
| 37 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 846,135 | 419,150 | 2.02 | 11 hrs 53 mins |
| 38 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 766,987 | 364,283 | 2.11 | 11 hrs 24 mins |
| 39 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 713,703 | 394,849 | 1.81 | 13 hrs 17 mins |
|
|
|||||||
| 40 | Radeon RX 5500/5500M / Pro 5500M Navi 14 [Radeon RX 5500/5500M / Pro 5500M] |
AMD | Navi 14 | 564,572 | 365,476 | 1.54 | 15 hrs 32 mins |
| 41 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 451,827 | 356,431 | 1.27 | 18 hrs 56 mins |
| 42 | T600 TU117GL [T600] |
Nvidia | TU117GL | 288,958 | 293,288 | 0.99 | 24 hrs 22 mins |
| 43 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 283,999 | 324,374 | 0.88 | 27 hrs 25 mins |
| 44 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 183,268 | 245,464 | 0.75 | 32 hrs 9 mins |
| 45 | Radeon R9 200 Series Hawaii [Radeon R9 200 Series] |
AMD | Hawaii | 111,953 | 241,000 | 0.46 | 51 hrs 40 mins |
| 46 | Radeon WX 3100 [Radeon WX 3100] |
AMD | 101,651 | 241,000 | 0.42 | 56 hrs 54 mins | |
| 47 | Radeon R7/R6/R5 Series Carrizo [Radeon R7/R6/R5 Series] |
AMD | Carrizo | 34,111 | 241,000 | 0.14 | 169 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:38|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|