RESEARCH: CANCER
FOLDING PROJECT #17802 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Memorial Sloan Kettering Cancer Center
Project URL: View Project Website
WORK UNIT INFO
Atoms: 22,500Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores using AI to understand how proteins work, specifically focusing on BCL-XL, a protein linked to lymphoma. Researchers are testing new methods called 'adaptive sampling' to build models of proteins from different data points. One version starts with many protein structures and connects them, while another starts with just one and predicts the rest.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
BCL-XL apoptotic protein --- a drug target in lymphoma.
More info coming soon.
Together with project 17800 this is also testing new methodology --- adaptive sampling.
This is the adaptive (first time on F@h) sampling version of the system. Project 17801 tests a 'simpler' version of the problem for the adaptive sampling algorithm --- we start from 35 crystal structures and attempt to connect them into one model. Project 17802 tests a 'harder' version of the problem --- we start from just 1 crystal structure and attempt to predict the other ones.
RELATED TERMS GLOSSARY AI BETA
BCL-XL
B Cell lymphoma 2-associated X protein
BCL-XL is a protein that plays a role in preventing cell death. It is often overexpressed in cancer cells, making it a potential drug target.
apoptotic protein
A protein involved in programmed cell death (apoptosis).
Apoptotic proteins are essential for regulating cell death. They are involved in a complex cascade of events that lead to the dismantling and removal of damaged or unnecessary cells.
lymphoma
A type of cancer that affects the lymphatic system.
Lymphoma is a group of cancers that begin in the lymphocytes, a type of white blood cell that helps fight infection. It can affect lymph nodes, spleen, bone marrow, and other tissues.
drug target
A molecule or biological pathway that is a potential therapeutic target for a drug.
Drug targets are often proteins or enzymes involved in disease processes. Developing drugs that interact with these targets can help treat or prevent diseases.
adaptive sampling
A method of selecting data points for analysis based on their properties.
Adaptive sampling is a technique used to improve the efficiency of computational simulations and analyses. It involves dynamically adjusting the selection criteria for data points based on the evolving characteristics of the system being studied.
crystal structure
The three-dimensional arrangement of atoms in a molecule.
Crystal structures reveal the precise arrangement of atoms within molecules. This information is crucial for understanding protein function and designing drugs that target specific proteins.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:54|
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 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,897,763 | 115,133 | 16.48 | 1 hrs 27 mins |
| 2 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,778,539 | 111,243 | 15.99 | 1 hrs 30 mins |
| 3 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,697,619 | 110,849 | 15.31 | 1 hrs 34 mins |
| 4 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,558,931 | 107,902 | 14.45 | 1 hrs 40 mins |
| 5 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,348,497 | 102,753 | 13.12 | 1 hrs 50 mins |
| 6 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 958,773 | 91,656 | 10.46 | 2 hrs 18 mins |
| 7 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 863,937 | 88,044 | 9.81 | 2 hrs 27 mins |
| 8 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 709,933 | 83,057 | 8.55 | 2 hrs 48 mins |
| 9 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 693,376 | 69,987 | 9.91 | 2 hrs 25 mins |
| 10 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 681,419 | 81,825 | 8.33 | 2 hrs 53 mins |
| 11 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 657,260 | 80,938 | 8.12 | 2 hrs 57 mins |
| 12 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 591,798 | 77,957 | 7.59 | 3 hrs 10 mins |
| 13 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 583,328 | 78,589 | 7.42 | 3 hrs 14 mins |
| 14 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 296,308 | 61,968 | 4.78 | 5 hrs 1 mins |
| 15 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 289,008 | 64,558 | 4.48 | 5 hrs 22 mins |
| 16 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 266,257 | 59,954 | 4.44 | 5 hrs 24 mins |
| 17 | GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634 |
Nvidia | GK104 | 234,146 | 57,403 | 4.08 | 5 hrs 53 mins |
| 18 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 203,822 | 54,787 | 3.72 | 6 hrs 27 mins |
| 19 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 157,030 | 50,265 | 3.12 | 7 hrs 41 mins |
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| 20 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 148,361 | 49,142 | 3.02 | 7 hrs 57 mins |
| 21 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 129,825 | 47,053 | 2.76 | 8 hrs 42 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:54|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|