RESEARCH: PROTEIN-DYNAMICS-DATASET
FOLDING PROJECT #17653 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 wants to create a huge dataset of moving proteins. This will help AI learn how proteins work. The dataset will include small, medium, and large proteins with different shapes.
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: medium sized proteins (40,000 atoms)
17653: Large sized proteins (70,000 atoms).
RELATED TERMS GLOSSARY AI BETA
AlphaFold2
A deep learning algorithm for protein structure prediction
AlphaFold2 is a powerful AI system that can predict the 3D shape of proteins from their amino acid sequence. This has revolutionized protein research by allowing scientists to understand how proteins fold and function.
Protein
A large biomolecule composed of chains of amino acids.
Proteins are the workhorses of our cells, carrying out a vast array of functions such as catalyzing reactions, transporting molecules, and providing structural support. Their shape is crucial to their function.
Topology
The arrangement of atoms or parts in a molecule.
Protein topology refers to the 3D arrangement of amino acids within a protein molecule. Understanding protein topology is essential for comprehending how proteins fold and interact with other molecules.
Dynamics
The movement and changes in shape of molecules over time.
Protein dynamics refers to the constant motion and flexibility of proteins. This dynamic behavior is crucial for protein function, allowing them to interact with other molecules and carry out their roles.
Conformation
A specific three-dimensional arrangement of a molecule.
A protein's conformation is its unique 3D shape. Proteins can exist in different conformations, and these changes are often involved in their function.
Amino Acid
The building blocks of proteins.
Amino acids are organic molecules that link together to form polypeptide chains, which then fold into proteins. There are 20 different amino acids commonly found in proteins.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:37:27|
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 | 17,304,432 | 20,450 | 846.18 | 0 hrs 2 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 15,995,482 | 21,861 | 731.69 | 0 hrs 2 mins |
| 3 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 13,417,039 | 593,874 | 22.59 | 1 hrs 4 mins |
| 4 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 13,385,646 | 149,379 | 89.61 | 0 hrs 16 mins |
| 5 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 13,005,572 | 33,342 | 390.07 | 0 hrs 4 mins |
| 6 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 11,474,295 | 20,450 | 561.09 | 0 hrs 3 mins |
| 7 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,223,984 | 20,450 | 402.15 | 0 hrs 4 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 6,571,292 | 216,237 | 30.39 | 0 hrs 47 mins |
| 9 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 6,362,725 | 20,450 | 311.14 | 0 hrs 5 mins |
| 10 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 6,154,054 | 182,586 | 33.70 | 0 hrs 43 mins |
| 11 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,134,336 | 209,134 | 29.33 | 0 hrs 49 mins |
| 12 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 5,240,419 | 434,962 | 12.05 | 1 hrs 60 mins |
| 13 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 5,225,731 | 20,450 | 255.54 | 0 hrs 6 mins |
| 14 | RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation] |
Nvidia | AD104GL | 4,674,241 | 20,450 | 228.57 | 0 hrs 6 mins |
| 15 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 4,298,914 | 105,396 | 40.79 | 0 hrs 35 mins |
| 16 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,925,928 | 20,450 | 191.98 | 0 hrs 8 mins |
| 17 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,160,531 | 24,468 | 129.17 | 0 hrs 11 mins |
| 18 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,463,068 | 20,450 | 120.44 | 0 hrs 12 mins |
| 19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,445,719 | 155,364 | 15.74 | 1 hrs 31 mins |
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| 20 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 2,408,383 | 56,600 | 42.55 | 0 hrs 34 mins |
| 21 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,046,224 | 117,597 | 17.40 | 1 hrs 23 mins |
| 22 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,573,246 | 133,341 | 11.80 | 2 hrs 2 mins |
| 23 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,481,825 | 131,618 | 11.26 | 2 hrs 8 mins |
| 24 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,426,748 | 20,450 | 69.77 | 0 hrs 21 mins |
| 25 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 1,391,373 | 20,450 | 68.04 | 0 hrs 21 mins |
| 26 | GeForce RTX 3050 Ti Mobile GA107M [GeForce RTX 3050 Ti Mobile] |
Nvidia | GA107M | 1,187,455 | 122,497 | 9.69 | 2 hrs 29 mins |
| 27 | RTX A1000 GA107GL [RTX A1000] |
Nvidia | GA107GL | 1,175,007 | 20,450 | 57.46 | 0 hrs 25 mins |
| 28 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 1,160,676 | 20,450 | 56.76 | 0 hrs 25 mins |
| 29 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,124,494 | 20,450 | 54.99 | 0 hrs 26 mins |
| 30 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 851,607 | 109,988 | 7.74 | 3 hrs 6 mins |
| 31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 820,706 | 20,450 | 40.13 | 0 hrs 36 mins |
| 32 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 761,989 | 20,450 | 37.26 | 0 hrs 39 mins |
| 33 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 683,175 | 100,560 | 6.79 | 3 hrs 32 mins |
| 34 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 661,580 | 20,450 | 32.35 | 0 hrs 45 mins |
| 35 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 638,637 | 20,450 | 31.23 | 0 hrs 46 mins |
| 36 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 613,685 | 20,450 | 30.01 | 0 hrs 48 mins |
| 37 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 525,367 | 20,450 | 25.69 | 0 hrs 56 mins |
| 38 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 506,034 | 73,865 | 6.85 | 3 hrs 30 mins |
| 39 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 475,479 | 85,555 | 5.56 | 4 hrs 19 mins |
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| 40 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 472,061 | 20,450 | 23.08 | 1 hrs 2 mins |
| 41 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 379,306 | 83,330 | 4.55 | 5 hrs 16 mins |
| 42 | R9 380X/R9 M295X Tonga XT/Amethyst XT [R9 380X/R9 M295X] |
AMD | Tonga XT/Amethyst XT | 267,393 | 75,462 | 3.54 | 6 hrs 46 mins |
| 43 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 257,410 | 70,088 | 3.67 | 6 hrs 32 mins |
| 44 | RX Vega M GL Polaris 22 XL [RX Vega M GL] |
AMD | Polaris 22 XL | 210,979 | 20,450 | 10.32 | 2 hrs 20 mins |
| 45 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 191,019 | 66,611 | 2.87 | 8 hrs 22 mins |
| 46 | Radeon RX 460/560D Baffin [Radeon RX 460/560D] |
AMD | Baffin | 156,775 | 20,450 | 7.67 | 3 hrs 8 mins |
| 47 | Ryzen 7000 Series iGPU Raphael [Ryzen 7000 Series iGPU] |
AMD | Raphael | 121,060 | 37,593 | 3.22 | 7 hrs 27 mins |
| 48 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 104,076 | 54,470 | 1.91 | 12 hrs 34 mins |
| 49 | Vega Mobile APU Lucienne [Vega Mobile APU] |
AMD | Lucienne | 12,781 | 23,766 | 0.54 | 44 hrs 38 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:37:27|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | CORE I7-8705G CPU @ 3.10GHZ | 8 | Intel |