RESEARCH: CANCER
FOLDING PROJECT #18103 PROFILE

PROJECT TEAM

Manager(s): Rafal Wiewiora
Institution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website

WORK UNIT INFO

Atoms: 19,724
Core: 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 work against KRas and develop new treatments. Public data and research findings 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, 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

Note: Glossary items are a high level summary and may not be 100% accurate.

KRas

Kirsten rat sarcoma viral oncogene homolog

Technical: Biotechnology
Oncology / Cancer Research

KRas is a protein that plays a crucial role in cell growth and division. Mutations in the KRAS gene can lead to uncontrolled cell growth, causing various types of cancer.


GDP

Guanosine diphosphate

Scientific: Pharmaceuticals
Biochemistry / Molecular Biology

GDP is a molecule that binds to proteins like KRas, turning them off and inhibiting cell growth. It's involved in signaling pathways within cells.


GTP

Guanosine triphosphate

Scientific: Pharmaceuticals
Biochemistry / Molecular Biology

GTP is a molecule that activates proteins like KRas, promoting cell growth and division. It's involved in energy transfer within cells.


Protein

Large biomolecules composed of chains of amino acids.

Technical: Pharmaceuticals
Biochemistry / Molecular Biology

Proteins are essential building blocks of cells and perform a wide variety of functions, including catalyzing reactions, transporting molecules, and providing structural support.


Cancer

A group of diseases characterized by abnormal cell growth and spread.

Clinical: Healthcare
Oncology / Disease

Cancer is a complex disease caused by mutations in genes that control cell growth and division. These mutations can lead to uncontrolled cell proliferation, invasion into surrounding tissues, and metastasis (spread to other parts of the body).

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:32:26
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 3,734,565 240,593 15.52 1 hrs 33 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 2,827,651 124,005 22.80 1 hrs 3 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 2,804,721 122,139 22.96 1 hrs 3 mins
4 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 2,307,827 116,368 19.83 1 hrs 13 mins
5 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 2,298,864 116,653 19.71 1 hrs 13 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 2,178,958 113,555 19.19 1 hrs 15 mins
7 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 2,142,283 113,686 18.84 1 hrs 16 mins
8 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 2,034,409 110,973 18.33 1 hrs 19 mins
9 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 1,901,808 108,934 17.46 1 hrs 22 mins
10 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 1,898,120 109,334 17.36 1 hrs 23 mins
11 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 1,821,669 108,038 16.86 1 hrs 25 mins
12 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,771,013 106,361 16.65 1 hrs 26 mins
13 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 1,683,634 105,341 15.98 1 hrs 30 mins
14 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 1,547,524 102,085 15.16 1 hrs 35 mins
15 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 1,515,781 101,163 14.98 1 hrs 36 mins
16 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 1,503,186 101,249 14.85 1 hrs 37 mins
17 Tesla P40
GP102GL [Tesla P40] 11760
Nvidia GP102GL 1,471,374 99,948 14.72 1 hrs 38 mins
18 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 1,456,411 100,139 14.54 1 hrs 39 mins
19 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,424,264 99,448 14.32 1 hrs 41 mins
20 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,397,408 98,812 14.14 1 hrs 42 mins
21 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,357,379 96,746 14.03 1 hrs 43 mins
22 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,300,974 96,358 13.50 1 hrs 47 mins
23 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,272,058 95,699 13.29 1 hrs 48 mins
24 GeForce RTX 3070 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,094,284 88,767 12.33 1 hrs 57 mins
25 Quadro RTX 6000/8000
TU102GL [Quadro RTX 6000/8000]
Nvidia TU102GL 1,052,537 90,148 11.68 2 hrs 3 mins
26 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,051,448 89,816 11.71 2 hrs 3 mins
27 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,014,937 89,072 11.39 2 hrs 6 mins
28 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 871,864 68,742 12.68 1 hrs 54 mins
29 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 778,234 78,503 9.91 2 hrs 25 mins
30 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 587,018 74,055 7.93 3 hrs 2 mins
31 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 527,370 70,584 7.47 3 hrs 13 mins
32 GeForce GTX 1650
TU117 [GeForce GTX 1650] 3091
Nvidia TU117 518,777 71,509 7.25 3 hrs 18 mins
33 GeForce GTX 1650
TU116 [GeForce GTX 1650] 2984
Nvidia TU116 472,877 68,595 6.89 3 hrs 29 mins
34 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 442,191 67,153 6.58 3 hrs 39 mins
35 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 363,460 63,435 5.73 4 hrs 11 mins
36 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 272,010 56,698 4.80 5 hrs 0 mins
37 P104-100
GP104 [P104-100]
Nvidia GP104 254,454 56,106 4.54 5 hrs 18 mins
38 GeForce GTX 1050 Ti Mobile
GP107M [GeForce GTX 1050 Ti Mobile]
Nvidia GP107M 231,179 54,510 4.24 5 hrs 40 mins
39 P106-100
GP106 [P106-100]
Nvidia GP106 199,348 51,700 3.86 6 hrs 13 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:32:26
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make