RESEARCH: CANCER
FOLDING PROJECT #18041 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 170,000Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Scientists are studying KRas, a protein that controls cell growth and is often mutated in cancer. The project uses computer simulations to understand how drugs interact with KRas, aiming to develop new cancer treatments. Public data and insights will be shared.
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
A protein involved in cell growth and division, often mutated in cancers.
KRas is a protein that acts like a switch, controlling cell growth. When it's on, cells grow and divide. But when it's off, they don't. Mutations in the KRas gene can keep it stuck in the 'on' position, leading to uncontrolled cell growth and cancer.
GDP
Guanosine Diphosphate
GDP is a molecule that binds to KRas and turns it 'off', preventing cell growth. Think of it like a brake pedal for cells.
GTP
Guanosine Triphosphate
GTP is a molecule that binds to KRas and turns it 'on', allowing cell growth. Think of it like the gas pedal for cells.
Inhibitors
Substances that block the activity of a protein or enzyme.
Inhibitors are drugs designed to stop KRas from working properly, thus slowing down cancer cell growth.
Mutations
Permanent changes in the DNA sequence of a gene.
Mutations in KRas can cause it to become permanently 'on', leading to uncontrolled cell growth and cancer.
Prognoses
The likely course or outcome of a disease.
Prognoses refer to the predicted chances of recovery or survival for a patient with a specific disease.
Drug Design
The process of creating new drugs.
Drug design involves scientists using their knowledge of biology and chemistry to create molecules that can treat diseases.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:35|
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 | 8,632,860 | 1,132,522 | 7.62 | 3 hrs 9 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,441,171 | 1,000,457 | 6.44 | 3 hrs 44 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,131,331 | 1,007,363 | 6.09 | 3 hrs 57 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,511,789 | 968,998 | 5.69 | 4 hrs 13 mins |
| 5 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,037,425 | 933,403 | 5.40 | 4 hrs 27 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,239,994 | 908,066 | 4.67 | 5 hrs 8 mins |
| 7 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,103,953 | 890,043 | 4.61 | 5 hrs 12 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,830,131 | 860,164 | 4.45 | 5 hrs 23 mins |
| 9 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,642,756 | 862,457 | 4.22 | 5 hrs 41 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,257,803 | 804,293 | 4.05 | 5 hrs 56 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,948,698 | 727,219 | 4.05 | 5 hrs 55 mins |
| 12 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,484,809 | 731,039 | 3.40 | 7 hrs 4 mins |
| 13 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,427,172 | 749,726 | 3.24 | 7 hrs 25 mins |
| 14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,365,815 | 732,642 | 3.23 | 7 hrs 26 mins |
| 15 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 2,138,437 | 709,959 | 3.01 | 7 hrs 58 mins |
| 16 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,963,531 | 677,983 | 2.90 | 8 hrs 17 mins |
| 17 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,850,912 | 672,885 | 2.75 | 8 hrs 44 mins |
| 18 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,844,271 | 693,601 | 2.66 | 9 hrs 2 mins |
| 19 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,799,065 | 671,354 | 2.68 | 8 hrs 57 mins |
|
|
|||||||
| 20 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,779,377 | 685,753 | 2.59 | 9 hrs 15 mins |
| 21 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,734,244 | 712,748 | 2.43 | 9 hrs 52 mins |
| 22 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,558,354 | 658,311 | 2.37 | 10 hrs 8 mins |
| 23 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,484,225 | 619,862 | 2.39 | 10 hrs 1 mins |
| 24 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,454,838 | 625,065 | 2.33 | 10 hrs 19 mins |
| 25 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,354,776 | 628,094 | 2.16 | 11 hrs 8 mins |
| 26 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,142,720 | 576,381 | 1.98 | 12 hrs 6 mins |
| 27 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,052,938 | 559,651 | 1.88 | 12 hrs 45 mins |
| 28 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 1,029,333 | 577,037 | 1.78 | 13 hrs 27 mins |
| 29 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,003,514 | 578,677 | 1.73 | 13 hrs 50 mins |
| 30 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 969,057 | 544,186 | 1.78 | 13 hrs 29 mins |
| 31 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 941,007 | 574,727 | 1.64 | 14 hrs 39 mins |
| 32 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 917,117 | 534,112 | 1.72 | 13 hrs 59 mins |
| 33 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 852,151 | 551,491 | 1.55 | 15 hrs 32 mins |
| 34 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 825,244 | 544,416 | 1.52 | 15 hrs 50 mins |
| 35 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 692,283 | 487,162 | 1.42 | 16 hrs 53 mins |
| 36 | Radeon RX 5500/5500M / Pro 5500M Navi 14 [Radeon RX 5500/5500M / Pro 5500M] |
AMD | Navi 14 | 499,776 | 436,882 | 1.14 | 20 hrs 59 mins |
| 37 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 444,916 | 403,136 | 1.10 | 21 hrs 45 mins |
| 38 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 410,265 | 409,124 | 1.00 | 23 hrs 56 mins |
| 39 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 399,879 | 378,815 | 1.06 | 22 hrs 44 mins |
|
|
|||||||
| 40 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 328,339 | 407,024 | 0.81 | 29 hrs 45 mins |
| 41 | Radeon HD 7800 Series Pitcairn PRO [Radeon HD 7800 Series] |
AMD | Pitcairn PRO | 177,251 | 335,000 | 0.53 | 45 hrs 22 mins |
| 42 | Radeon Vega Series / Radeon Vega Mobile Series Raven Ridge [Radeon Vega Series / Radeon Vega Mobile Series] |
AMD | Raven Ridge | 69,573 | 335,000 | 0.21 | 115 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:35|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|