RESEARCH: PROTEIN-DYNAMICS-MODELING
FOLDING PROJECT #19505 PROFILE
PROJECT TEAM
Manager(s): Andreas KrämerInstitution: Freie Universität Berlin
Project URL: View Project Website
WORK UNIT INFO
Atoms: 34,702Core: 0x22
Status: Public
Related Projects
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
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.
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.
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.
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.
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.
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.
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.
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 |
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| 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 |
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| 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 |
|---|