RESEARCH: CANCER
FOLDING PROJECT #17805 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Memorial Sloan Kettering Cancer Center
Project URL: View Project Website
WORK UNIT INFO
Atoms: 80,000Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies BAX, a protein important for cell death in lymphoma. It's similar to another protein studied before, but this one changes shape dramatically when activated. This makes understanding it harder, but could lead to new cancer treatments.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
BAX apoptotic protein --- a drug target in lymphoma. This is a homologous (i.e shares some structures) protein to BCL in projects 17800-03 --- as in there, I am testing adaptive sampling strategies --- this project is 'vanilla' (i.e.
no adaptive algorithm) and the problem here is probably much harder than in the previous project, this particular protein experiences 'activation' which is a massive structural change, a long tail unbinds and extends away from the protein.
RELATED TERMS GLOSSARY AI BETA
BAX apoptotic protein
A pro-apoptotic protein involved in programmed cell death.
BAX is a protein that plays a crucial role in triggering apoptosis, the process of programmed cell death. It is found to be dysregulated in various cancers, including lymphoma. Research focuses on BAX as a potential drug target for cancer therapies.
BCL
Bcl-2 family of proteins regulating apoptosis.
The Bcl-2 protein family consists of various proteins that play a critical role in controlling cell death (apoptosis). Some members promote cell survival, while others, like BAX, induce apoptosis. Understanding the interactions within this family is crucial for developing targeted cancer therapies.
Adaptive Sampling
A technique for selecting protein structures in simulations.
Adaptive sampling is a computational method used to improve the efficiency of protein modeling simulations. It involves dynamically adjusting the selection criteria for protein structures during simulations, focusing on regions that are more relevant for understanding protein function or folding.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:50|
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 | 5,234,965 | 230,117 | 22.75 | 1 hrs 3 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,940,000 | 222,986 | 22.15 | 1 hrs 5 mins |
| 3 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,934,145 | 208,619 | 18.86 | 1 hrs 16 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,813,710 | 207,263 | 18.40 | 1 hrs 18 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 2,827,602 | 187,529 | 15.08 | 1 hrs 36 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 2,455,842 | 179,040 | 13.72 | 1 hrs 45 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,258,080 | 173,951 | 12.98 | 1 hrs 51 mins |
| 8 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,142,147 | 171,067 | 12.52 | 1 hrs 55 mins |
| 9 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,081,956 | 169,531 | 12.28 | 1 hrs 57 mins |
| 10 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,081,202 | 169,564 | 12.27 | 1 hrs 57 mins |
| 11 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,986,379 | 166,506 | 11.93 | 2 hrs 1 mins |
| 12 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 1,879,007 | 163,865 | 11.47 | 2 hrs 6 mins |
| 13 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,850,532 | 162,938 | 11.36 | 2 hrs 7 mins |
| 14 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 1,833,398 | 162,536 | 11.28 | 2 hrs 8 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,585,674 | 152,650 | 10.39 | 2 hrs 19 mins |
| 16 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,476,840 | 151,552 | 9.74 | 2 hrs 28 mins |
| 17 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,397,173 | 148,543 | 9.41 | 2 hrs 33 mins |
| 18 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,150,599 | 139,125 | 8.27 | 2 hrs 54 mins |
| 19 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,090,591 | 137,061 | 7.96 | 3 hrs 1 mins |
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| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,034,177 | 134,456 | 7.69 | 3 hrs 7 mins |
| 21 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 994,714 | 132,202 | 7.52 | 3 hrs 11 mins |
| 22 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 985,078 | 131,379 | 7.50 | 3 hrs 12 mins |
| 23 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 286,966 | 87,581 | 3.28 | 7 hrs 19 mins |
| 24 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 252,604 | 83,794 | 3.01 | 7 hrs 58 mins |
| 25 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 247,328 | 83,370 | 2.97 | 8 hrs 5 mins |
| 26 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 121,153 | 65,088 | 1.86 | 12 hrs 54 mins |
| 27 | GeForce GT 840M GM108 [GeForce GT 840M] |
Nvidia | GM108 | 39,980 | 43,053 | 0.93 | 25 hrs 51 mins |
| 28 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 30,580 | 40,542 | 0.75 | 31 hrs 49 mins |
| 29 | GeForce 920M GK208 [GeForce 920M] |
Nvidia | GK208 | 26,734 | 39,832 | 0.67 | 35 hrs 46 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:50|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|