RESEARCH: PROTEIN-DYNAMICS-DATASET
FOLDING PROJECT #17655 PROFILE

PROJECT TEAM

Manager(s): Sukrit Singh
Institution: Memorial Sloan-Kettering Cancer-Center
Project URL: View Project Website

WORK UNIT INFO

Atoms: 70,000
Core: 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

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

AI

Artificial Intelligence

Technical: Technology
Biotechnology / Protein Structure Prediction

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.

Scientific: Pharmaceutical
Biotechnology / Structural Biology

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.

Scientific: Life Sciences
Biotechnology / Protein Structure Prediction

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.

Scientific: Pharmaceutical
Biotechnology / Molecular Simulations

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.

Scientific: Life Sciences
Biotechnology / Protein Structure Prediction

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.

Technical: Artificial Intelligence
Biotechnology / Protein Structure Prediction

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
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
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