RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12103 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 41,000Core: 0x22
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
This project explores the different shapes short chains of amino acids (peptides) can take. By using computer models and machine learning, we aim to understand how these shapes influence their function. This knowledge could be used to design new peptides for applications like creating materials inspired by biology.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Peptide Conformations Short peptides can be used to observe possible conformations, which are important factors for determining their function.
Insights from these simulations can then be further extended to larger peptides.
In this study, we aim to explore the conformational space of all possible short peptides and using modeling techniques like machine learning to gain insights.
These can then be used for rational design of peptides for applications in biologically-inspired materials.
RELATED TERMS GLOSSARY AI BETA
Peptide
A short chain of amino acids linked together.
A peptide is a short chain of amino acids connected by chemical bonds. Peptides are important building blocks of proteins and have various functions in living organisms, such as signaling, transporting molecules, and defending against infections.
Conformations
The different shapes that a molecule can take.
Conformations refer to the various three-dimensional shapes that molecules can adopt. These shapes are crucial for a molecule's function, as they determine how it interacts with other molecules.
Simulations
Computer models that mimic real-world processes.
Simulations are computer programs that create virtual environments to study and predict how systems behave. In biotechnology, simulations are used to model biological processes, such as protein folding or drug interactions.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning is a branch of artificial intelligence where computers are trained on large datasets to identify patterns and make predictions. In biotechnology, machine learning is used for tasks such as drug discovery, disease diagnosis, and personalized medicine.
Rational Design
A method of designing molecules with specific properties.
Rational design is a process used in drug development to create molecules with desired properties. Scientists use their knowledge of biological targets and molecular interactions to design compounds that bind to specific proteins or enzymes.
Biologically-inspired Materials
Materials that are designed based on biological structures or processes.
Biologically-inspired materials are developed by mimicking the structure and function of natural materials. These materials often exhibit unique properties such as strength, flexibility, and self-healing capabilities.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:53|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 3,186,660 | 406,216 | 7.84 | 3 hrs 4 mins |
| 2 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,348,394 | 353,111 | 6.65 | 3 hrs 37 mins |
| 3 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,031,761 | 317,786 | 6.39 | 3 hrs 45 mins |
| 4 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,991,001 | 345,070 | 5.77 | 4 hrs 10 mins |
| 5 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,597,512 | 326,165 | 4.90 | 4 hrs 54 mins |
| 6 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,450,361 | 326,060 | 4.45 | 5 hrs 24 mins |
| 7 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 1,383,466 | 301,612 | 4.59 | 5 hrs 14 mins |
| 8 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,373,938 | 313,412 | 4.38 | 5 hrs 28 mins |
| 9 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,296,716 | 304,902 | 4.25 | 5 hrs 39 mins |
| 10 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,157,992 | 277,848 | 4.17 | 5 hrs 46 mins |
| 11 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,135,093 | 288,060 | 3.94 | 6 hrs 5 mins |
| 12 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,044,136 | 280,820 | 3.72 | 6 hrs 27 mins |
| 13 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 990,621 | 296,717 | 3.34 | 7 hrs 11 mins |
| 14 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 932,320 | 269,666 | 3.46 | 6 hrs 57 mins |
| 15 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 813,995 | 257,493 | 3.16 | 7 hrs 36 mins |
| 16 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 785,972 | 248,214 | 3.17 | 7 hrs 35 mins |
| 17 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 735,118 | 249,429 | 2.95 | 8 hrs 9 mins |
| 18 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 626,612 | 235,279 | 2.66 | 9 hrs 1 mins |
| 19 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 605,163 | 254,280 | 2.38 | 10 hrs 5 mins |
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| 20 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 527,355 | 209,632 | 2.52 | 9 hrs 32 mins |
| 21 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 516,832 | 230,693 | 2.24 | 10 hrs 43 mins |
| 22 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 513,144 | 139,786 | 3.67 | 6 hrs 32 mins |
| 23 | GeForce GTX 1060 Mobile GP106M [GeForce GTX 1060 Mobile] |
Nvidia | GP106M | 504,666 | 219,973 | 2.29 | 10 hrs 28 mins |
| 24 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 487,074 | 217,871 | 2.24 | 10 hrs 44 mins |
| 25 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 443,719 | 234,043 | 1.90 | 12 hrs 40 mins |
| 26 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 358,857 | 196,833 | 1.82 | 13 hrs 10 mins |
| 27 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 309,347 | 214,308 | 1.44 | 16 hrs 38 mins |
| 28 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 302,431 | 185,670 | 1.63 | 14 hrs 44 mins |
| 29 | RX Vega M GL Polaris 22 XL [RX Vega M GL] |
AMD | Polaris 22 XL | 179,211 | 155,907 | 1.15 | 20 hrs 53 mins |
| 30 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 161,803 | 150,286 | 1.08 | 22 hrs 18 mins |
| 31 | GeForce MX110 GM108M [GeForce MX110] |
Nvidia | GM108M | 118,835 | 118,835 | 1.00 | 24 hrs 0 mins |
| 32 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 114,049 | 128,167 | 0.89 | 26 hrs 58 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:53|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|