RESEARCH: CANCER
FOLDING PROJECT #18102 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 19,724Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Scientists are studying KRas, a protein that helps control cell growth and is often mutated in cancer. The project uses computer simulations to understand how drugs work against KRas and develop new treatments. All data will be shared publicly.
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 whether cells grow and divide. When it's switched on, it tells cells to multiply. In many cancers, KRas becomes mutated and stays stuck in the 'on' position, causing uncontrolled cell growth.
GDP
Guanosine diphosphate
GDP is a molecule that binds to KRas when it's in its 'off' state, preventing cell growth and division.
GTP
Guanosine triphosphate
GTP is a molecule that binds to KRas when it's in its 'on' state, signaling cell growth and division.
Inhibitors
Substances that block the activity of a target molecule.
Inhibitors are molecules designed to prevent KRas from functioning properly, potentially slowing or stopping cancer growth.
Drug Design
The process of discovering and developing new medications.
Drug design involves identifying molecules that can interact with specific targets like KRas to treat diseases.
Protein Folding
The process by which a protein assumes its three-dimensional shape.
Protein folding is essential for proteins to function correctly. Studying how proteins fold can help us understand diseases and develop new treatments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:28|
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 | 3,804,665 | 242,195 | 15.71 | 1 hrs 32 mins |
| 2 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,227,149 | 138,287 | 23.34 | 1 hrs 2 mins |
| 3 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,082,889 | 140,918 | 21.88 | 1 hrs 6 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 2,905,665 | 129,863 | 22.37 | 1 hrs 4 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 2,715,413 | 134,960 | 20.12 | 1 hrs 12 mins |
| 6 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 2,586,347 | 130,476 | 19.82 | 1 hrs 13 mins |
| 7 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,220,384 | 114,768 | 19.35 | 1 hrs 14 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,047,645 | 120,066 | 17.05 | 1 hrs 24 mins |
| 9 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,022,156 | 116,790 | 17.31 | 1 hrs 23 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,691,559 | 113,857 | 14.86 | 1 hrs 37 mins |
| 11 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 1,665,600 | 104,733 | 15.90 | 1 hrs 31 mins |
| 12 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,619,462 | 114,337 | 14.16 | 1 hrs 42 mins |
| 13 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,600,560 | 113,681 | 14.08 | 1 hrs 42 mins |
| 14 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 1,529,864 | 108,866 | 14.05 | 1 hrs 42 mins |
| 15 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,512,941 | 112,045 | 13.50 | 1 hrs 47 mins |
| 16 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,302,047 | 99,497 | 13.09 | 1 hrs 50 mins |
| 17 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,189,275 | 102,199 | 11.64 | 2 hrs 4 mins |
| 18 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 617,241 | 76,472 | 8.07 | 2 hrs 58 mins |
| 19 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 520,636 | 78,300 | 6.65 | 3 hrs 37 mins |
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| 20 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 313,041 | 59,826 | 5.23 | 4 hrs 35 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:28|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|