RESEARCH: CANCER
FOLDING PROJECT #18042 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 190,000Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Scientists are studying how a protein called KRas works in cancer cells. KRas is like a switch that controls cell growth, and when it's broken, it can cause cancer to grow out of control. The project uses computer simulations to understand how drugs work against KRas and hopes to develop new treatments.
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. When mutated, it can lead to uncontrolled cell growth, a hallmark of cancer. Scientists are actively researching ways to target KRas for cancer treatment.
GDP
Guanosine Diphosphate
GDP is a molecule that binds to KRas, keeping it in an inactive state. When GDP is replaced with GTP, KRas becomes active and signals for cell growth.
GTP
Guanosine Triphosphate
GTP is a molecule that activates KRas, triggering cell growth and division signals. When GTP is converted back to GDP, KRas returns to its inactive state.
Inhibitor
A substance that blocks or reduces the activity of a specific target, such as a protein.
Inhibitors are used to block the activity of proteins involved in disease processes. In cancer treatment, inhibitors aim to stop the growth and spread of tumor cells.
Lumakras
A drug that inhibits KRas activity, approved for treating certain types of lung cancer.
Lumakras is a new medication used to treat patients with non-small cell lung cancer. It works by blocking the activity of the KRas protein, which drives uncontrolled cell growth in this type of cancer.
Folding@home
A distributed computing project that harnesses the power of volunteers' computers to simulate protein folding and other biological processes.
Folding@home is a collaborative research effort where individuals donate their computer processing power to help scientists understand how proteins fold and interact. This knowledge is crucial for developing new drugs and treatments.
Roivant Sciences
A biopharmaceutical company focused on developing and commercializing new drugs for various diseases.
Roivant Sciences is a pharmaceutical company dedicated to bringing innovative treatments to patients. They specialize in drug discovery and development, particularly in areas like oncology and rare diseases.
Amgen
A biotechnology company that develops and manufactures biologic medicines.
Amgen is a leading pharmaceutical company known for its expertise in developing and manufacturing biologics, a type of medicine derived from living organisms. Their portfolio includes treatments for various conditions, including cancer, inflammatory diseases, and bone disorders.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:34|
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 | 6,651,670 | 1,196,060 | 5.56 | 4 hrs 19 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,195,533 | 1,182,432 | 5.24 | 4 hrs 35 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,058,150 | 1,086,883 | 4.65 | 5 hrs 9 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,850,325 | 1,106,520 | 4.38 | 5 hrs 29 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,466,929 | 1,063,113 | 4.20 | 5 hrs 43 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,806,782 | 1,038,510 | 3.67 | 6 hrs 33 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,778,831 | 1,005,217 | 3.76 | 6 hrs 23 mins |
| 8 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,726,860 | 1,000,478 | 3.73 | 6 hrs 27 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,193,385 | 924,348 | 3.45 | 6 hrs 57 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,009,778 | 925,251 | 3.25 | 7 hrs 23 mins |
| 11 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,867,692 | 918,041 | 3.12 | 7 hrs 41 mins |
| 12 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,674,229 | 888,560 | 3.01 | 7 hrs 58 mins |
| 13 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,390,753 | 858,285 | 2.79 | 8 hrs 37 mins |
| 14 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 2,034,220 | 817,869 | 2.49 | 9 hrs 39 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,948,716 | 844,389 | 2.31 | 10 hrs 24 mins |
| 16 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,854,928 | 812,739 | 2.28 | 10 hrs 31 mins |
| 17 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,705,321 | 825,273 | 2.07 | 11 hrs 37 mins |
| 18 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,679,106 | 761,776 | 2.20 | 10 hrs 53 mins |
| 19 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,512,714 | 736,195 | 2.05 | 11 hrs 41 mins |
|
|
|||||||
| 20 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,477,932 | 734,963 | 2.01 | 11 hrs 56 mins |
| 21 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,297,507 | 699,461 | 1.86 | 12 hrs 56 mins |
| 22 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,236,287 | 688,254 | 1.80 | 13 hrs 22 mins |
| 23 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,045,088 | 681,397 | 1.53 | 15 hrs 39 mins |
| 24 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,037,394 | 644,683 | 1.61 | 14 hrs 55 mins |
| 25 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 909,760 | 619,163 | 1.47 | 16 hrs 20 mins |
| 26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 874,148 | 649,825 | 1.35 | 17 hrs 50 mins |
| 27 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 853,147 | 637,595 | 1.34 | 17 hrs 56 mins |
| 28 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 671,254 | 542,121 | 1.24 | 19 hrs 23 mins |
| 29 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 425,725 | 481,658 | 0.88 | 27 hrs 9 mins |
| 30 | Radeon RX 5500/5500M / Pro 5500M Navi 14 [Radeon RX 5500/5500M / Pro 5500M] |
AMD | Navi 14 | 420,518 | 479,265 | 0.88 | 27 hrs 21 mins |
| 31 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 347,669 | 461,224 | 0.75 | 31 hrs 50 mins |
| 32 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 244,062 | 420,000 | 0.58 | 41 hrs 18 mins |
| 33 | Radeon Vega Series / Radeon Vega Mobile Series Raven Ridge [Radeon Vega Series / Radeon Vega Mobile Series] |
AMD | Raven Ridge | 95,549 | 420,000 | 0.23 | 105 hrs 30 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:34|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|