RESEARCH: PROTEIN-DYNAMICS-MODELING
FOLDING PROJECT #19505 PROFILE

TLDR; PROJECT SUMMARY AI BETA

The project relates to building powerful AI tools to understand how proteins move and interact. This could lead to better ways to predict how viruses change and develop new treatments for diseases.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

Summary The AI4Science Group at Freie Universität Berlin develops machine learning methods for physical sciences, with a focus on physics-constrained learning algorithms, complex dynamical systems analysis, efficient generative learning methods for statistical mechanics, and highly accurate machine learning methods for quantum mechanics.

They are an interdisciplinary team of mathematicians, chemists, physicists, and computer scientists. Details The primary objective of this project is to develop large-scale artificial intelligence (AI) models to efficiently sample protein dynamics and predict the stability of folded states and protein-protein interactions.

Being able to do this efficiently and accurately would be a game-changer for the prediction of virus mutants and the design of therapies for various diseases.

AI techniques have demonstrated exceptional performance on benchmark systems and have the potential to vastly speed up computations yet maintain comparable levels of accuracy as classical molecular dynamics simulations. The project aims to generate a comprehensive dataset of small protein systems that will provide the necessary information for creating the next generation of AI models for protein simulations.

We will collaborate with the Clementi Group at Freie Universität Berlin to achieve this goal.

RELATED TERMS GLOSSARY AI BETA

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

artificial intelligence

A branch of computer science dealing with the creation of intelligent agents, such as computer systems that can reason, learn, and solve problems.

Technical: Technology
Computer Science / Machine Learning

Artificial intelligence (AI) involves creating computer systems that can mimic human intelligence. This includes tasks like learning from data, recognizing patterns, making decisions, and solving problems. AI is used in various fields, including healthcare, finance, and transportation.


protein dynamics

The motion and flexibility of protein molecules over time.

Scientific: Pharmaceuticals
Biotechnology / Structural Biology

Protein dynamics refers to the constant movement and flexibility of proteins. Understanding how proteins move and change shape is crucial for comprehending their function in biological processes. Scientists study protein dynamics using various techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy.


protein-protein interactions

The binding and association of two or more protein molecules.

Scientific: Pharmaceuticals
Biotechnology / Molecular Biology

Protein-protein interactions are essential for many biological processes, such as cell signaling, metabolism, and DNA replication. When proteins bind to each other, they can form complexes that carry out specific functions. Scientists study protein-protein interactions to understand how cells communicate and regulate their activities.


virus mutants

Variants of a virus that have acquired genetic changes, potentially altering their characteristics.

Scientific: Healthcare
Medicine / Virology

Virus mutants are variations of a virus that have undergone genetic mutations. These changes can affect the virus's ability to infect cells, spread, or evade the immune system. Scientists track virus mutants to understand how viruses evolve and develop new treatments.


therapies

Medical treatments designed to prevent, diagnose, or cure diseases.

Technical: Healthcare
Medicine / Pharmacology

Therapies are medical treatments used to address various health conditions. They can include medications, surgery, radiation therapy, and lifestyle changes. The goal of therapies is to improve patient outcomes and enhance their quality of life.


machine learning

A type of artificial intelligence that allows computers to learn from data without explicit programming.

Technical: Technology
Computer Science / Artificial Intelligence

Machine learning is a subset of artificial intelligence where algorithms learn from data to make predictions or decisions. Instead of being explicitly programmed, machine learning models are trained on large datasets, allowing them to identify patterns and relationships. This enables applications such as image recognition, natural language processing, and fraud detection.


quantum mechanics

A fundamental theory in physics that describes the behavior of matter and energy at the atomic and subatomic levels.

Scientific: Research
Physics / Quantum Theory

Quantum mechanics is a branch of physics that governs the behavior of particles at the smallest scales. It explains phenomena such as wave-particle duality, superposition, and quantum entanglement. Quantum mechanics has revolutionized our understanding of the universe and paved the way for technologies like lasers and transistors.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:24:28
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 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 3,535,786 143,778 24.59 0 hrs 59 mins
2 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 3,131,517 137,775 22.73 1 hrs 3 mins
3 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,450,118 121,889 20.10 1 hrs 12 mins
4 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,343,196 124,795 18.78 1 hrs 17 mins
5 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,331,356 124,940 18.66 1 hrs 17 mins
6 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 2,035,217 117,533 17.32 1 hrs 23 mins
7 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,804,814 114,990 15.70 1 hrs 32 mins
8 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,680,129 112,343 14.96 1 hrs 36 mins
9 GeForce RTX 3050 8GB
GA107 [GeForce RTX 3050 8GB]
Nvidia GA107 1,575,093 109,918 14.33 1 hrs 40 mins
10 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,513,853 108,167 14.00 1 hrs 43 mins
11 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,510,068 103,617 14.57 1 hrs 39 mins
12 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,487,320 107,063 13.89 1 hrs 44 mins
13 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,377,289 101,226 13.61 1 hrs 46 mins
14 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,343,859 101,418 13.25 1 hrs 49 mins
15 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,297,944 103,745 12.51 1 hrs 55 mins
16 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,173,746 98,966 11.86 2 hrs 1 mins
17 P104-100
GP104 [P104-100]
Nvidia GP104 1,134,970 97,324 11.66 2 hrs 3 mins
18 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 1,072,919 96,428 11.13 2 hrs 9 mins
19 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 1,023,353 94,905 10.78 2 hrs 14 mins
20 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 938,104 92,033 10.19 2 hrs 21 mins
21 GeForce GTX 1060 Mobile
GP106M [GeForce GTX 1060 Mobile]
Nvidia GP106M 883,774 90,420 9.77 2 hrs 27 mins
22 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 869,521 91,320 9.52 2 hrs 31 mins
23 P106-100
GP106 [P106-100]
Nvidia GP106 869,167 90,129 9.64 2 hrs 29 mins
24 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 867,149 89,650 9.67 2 hrs 29 mins
25 Quadro P3200 Mobile
GP104GLM [Quadro P3200 Mobile]
Nvidia GP104GLM 819,851 88,182 9.30 2 hrs 35 mins
26 Quadro P4000
GP104GL [Quadro P4000]
Nvidia GP104GL 797,305 85,101 9.37 2 hrs 34 mins
27 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 750,431 85,787 8.75 2 hrs 45 mins
28 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 718,696 85,225 8.43 2 hrs 51 mins
29 Quadro P2200
GP106GL [Quadro P2200]
Nvidia GP106GL 709,164 83,689 8.47 2 hrs 50 mins
30 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 646,132 78,723 8.21 2 hrs 55 mins
31 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 617,167 80,309 7.68 3 hrs 7 mins
32 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 574,493 78,222 7.34 3 hrs 16 mins
33 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 426,166 70,988 6.00 3 hrs 60 mins
34 GeForce GTX 1650 Ti Mobile
TU117M [GeForce GTX 1650 Ti Mobile]
Nvidia TU117M 415,826 70,488 5.90 4 hrs 4 mins
35 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 341,924 64,822 5.27 4 hrs 33 mins
36 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 293,971 61,916 4.75 5 hrs 3 mins
37 Quadro P620
GP107GL [Quadro P620]
Nvidia GP107GL 182,836 53,734 3.40 7 hrs 3 mins
38 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 172,676 52,327 3.30 7 hrs 16 mins
39 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 134,825 48,451 2.78 8 hrs 37 mins
40 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 48,075 34,398 1.40 17 hrs 10 mins
41 R7 370/R9 270X/370X
Curacao XT/Trinidad XT [R7 370/R9 270X/370X]
AMD Curacao XT/Trinidad XT 26,610 28,244 0.94 25 hrs 28 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 03:24:28
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make