RESEARCH: PROTEIN-DYNAMICS-DATASET
FOLDING PROJECT #17655 PROFILE
PROJECT TEAM
Manager(s): Sukrit SinghInstitution: Memorial Sloan-Kettering Cancer-Center
Project URL: View Project Website
WORK UNIT INFO
Atoms: 70,000Core: 0x26
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project makes datasets of protein movements for different sizes. This helps AI learn how proteins work and predict their shapes, which is important for things like medicine and biotechnology.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
With the explosion of AI-based models and architectures, a ripe opportunity presents itself to use these statistical models to help us bridge the gap large scale simulations and functional insight.
In particular, with the explosion of methods like AlphaFold2, there is a clear potential for these models to potentially predict dynamics, or use them to predict different conformations of a system at extremely large scales for a diverse set of sequences. However, for folks to be able to generate those kinds of models, a broad set of training data is needed that captures dynamics across a variety of different protein topologies.
This project seeks to generate that dataset - capturing dynamics of systems across a variety of different protein sizes and topologies.
17651: Small proteins (20,000 atoms)
17652 and 17654: medium sized proteins (40,000 atoms)
17653 and 17655: Large sized proteins (70,000 atoms).
RELATED TERMS GLOSSARY AI BETA
AI
Artificial Intelligence
A branch of computer science focused on creating intelligent systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, and decision-making.
Protein
A large biomolecule consisting of chains of amino acids.
Proteins are essential macromolecules that perform a vast array of functions within living organisms. They serve as structural components, catalyze biochemical reactions, transport molecules, and regulate cellular processes.
Topology
The arrangement of atoms or parts in a molecule or system.
Topology refers to the spatial relationships and connectivity between elements within a molecule or system. In protein structure prediction, topology describes the overall 3-dimensional arrangement of amino acids.
Dynamics
The motion and behavior of a system over time.
Dynamics encompasses the changes and movements of molecules or systems. In protein research, dynamics refers to the fluctuations and motions of proteins, influencing their function and interactions.
Conformation
A specific three-dimensional arrangement of a molecule.
Conformation describes the different spatial arrangements a molecule can adopt. Proteins exhibit various conformations that influence their function and interactions with other molecules.
AlphaFold2
An AI-powered system for predicting protein structures.
AlphaFold2 is a groundbreaking AI algorithm developed by DeepMind that revolutionized protein structure prediction. It leverages machine learning to accurately predict the 3D structures of proteins from their amino acid sequences.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:37:24|
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 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 17,130,904 | 21,868 | 783.38 | 0 hrs 2 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 16,738,418 | 160,410 | 104.35 | 0 hrs 14 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 16,043,553 | 25,868 | 620.21 | 0 hrs 2 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 12,091,376 | 153,105 | 78.97 | 0 hrs 18 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 8,922,231 | 193,420 | 46.13 | 0 hrs 31 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,572,234 | 75,648 | 113.32 | 0 hrs 13 mins |
| 7 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,464,135 | 217,223 | 38.97 | 0 hrs 37 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,152,290 | 222,438 | 32.15 | 0 hrs 45 mins |
| 9 | RTX 5000 Ada Generation Laptop GPU AD103GLM [RTX 5000 Ada Generation Laptop GPU] |
Nvidia | AD103GLM | 6,850,934 | 218,009 | 31.43 | 0 hrs 46 mins |
| 10 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 6,476,200 | 20,450 | 316.68 | 0 hrs 5 mins |
| 11 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,847,567 | 20,450 | 285.94 | 0 hrs 5 mins |
| 12 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,751,381 | 207,029 | 27.78 | 0 hrs 52 mins |
| 13 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 5,212,085 | 20,450 | 254.87 | 0 hrs 6 mins |
| 14 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,986,114 | 190,467 | 26.18 | 0 hrs 55 mins |
| 15 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,537,186 | 188,509 | 24.07 | 0 hrs 60 mins |
| 16 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,253,238 | 26,045 | 124.91 | 0 hrs 12 mins |
| 17 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,761,863 | 160,959 | 17.16 | 1 hrs 24 mins |
| 18 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 2,474,249 | 155,112 | 15.95 | 1 hrs 30 mins |
| 19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,323,038 | 153,413 | 15.14 | 1 hrs 35 mins |
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|
|||||||
| 20 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,247,470 | 20,450 | 109.90 | 0 hrs 13 mins |
| 21 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,190,614 | 72,946 | 30.03 | 0 hrs 48 mins |
| 22 | GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh] |
Nvidia | TU106M | 2,141,918 | 148,607 | 14.41 | 1 hrs 40 mins |
| 23 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,582,165 | 134,703 | 11.75 | 2 hrs 3 mins |
| 24 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,490,644 | 85,937 | 17.35 | 1 hrs 23 mins |
| 25 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,447,395 | 129,299 | 11.19 | 2 hrs 9 mins |
| 26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,328,546 | 57,803 | 22.98 | 1 hrs 3 mins |
| 27 | RTX A1000 GA107GL [RTX A1000] |
Nvidia | GA107GL | 1,180,986 | 20,450 | 57.75 | 0 hrs 25 mins |
| 28 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,170,744 | 38,067 | 30.75 | 0 hrs 47 mins |
| 29 | Quadro P3200 Mobile GP104GLM [Quadro P3200 Mobile] |
Nvidia | GP104GLM | 1,146,081 | 20,450 | 56.04 | 0 hrs 26 mins |
| 30 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 972,729 | 115,074 | 8.45 | 2 hrs 50 mins |
| 31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 834,273 | 20,450 | 40.80 | 0 hrs 35 mins |
| 32 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 803,609 | 107,794 | 7.46 | 3 hrs 13 mins |
| 33 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 755,466 | 20,450 | 36.94 | 0 hrs 39 mins |
| 34 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 606,033 | 20,450 | 29.63 | 0 hrs 49 mins |
| 35 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 375,442 | 50,101 | 7.49 | 3 hrs 12 mins |
| 36 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 296,951 | 76,938 | 3.86 | 6 hrs 13 mins |
| 37 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 289,305 | 34,113 | 8.48 | 2 hrs 50 mins |
| 38 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 85,769 | 24,175 | 3.55 | 6 hrs 46 mins |
| 39 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 76,123 | 20,450 | 3.72 | 6 hrs 27 mins |
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|
|||||||
| 40 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 73,254 | 20,450 | 3.58 | 6 hrs 42 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:37:24|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|