RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12105 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 41,000Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores the different shapes short peptides can take. By using computer models and machine learning, we can understand how these shapes affect their function. This knowledge can then be used to design new peptides for things like innovative materials.
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.
Peptides are essential building blocks in proteins. They are chains of amino acids that play diverse roles in biological processes. Understanding peptide structure and function is crucial for developing new drugs, materials, and diagnostics.
Conformations
The different 3-dimensional shapes that a molecule can take.
Conformations describe the various spatial arrangements of atoms within a molecule. For peptides, understanding their conformations is critical because shape dictates their function. Different conformations can lead to different interactions with other molecules.
Simulations
Computer models used to imitate complex systems.
Simulations are powerful tools in biotechnology to study biological processes. They allow researchers to model and predict how molecules interact and change over time without needing to perform expensive or time-consuming experiments.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning is revolutionizing biotechnology by enabling the analysis of vast datasets. Algorithms can identify patterns and make predictions, accelerating drug discovery, personalized medicine, and other applications.
Rational Design
The process of designing molecules with specific properties.
Rational design uses knowledge of molecular structure and function to create new molecules with desired effects. In drug discovery, it aims to develop safer and more effective medications.
Biologically-inspired Materials
Materials that are designed based on biological principles.
Biologically-inspired materials draw inspiration from nature's designs to create innovative and sustainable products. They can possess properties like self-healing, strength, or adaptability found in living organisms.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:52|
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 | 3,172,521 | 406,173 | 7.81 | 3 hrs 4 mins |
| 2 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,628,631 | 376,214 | 6.99 | 3 hrs 26 mins |
| 3 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,237,841 | 354,796 | 6.31 | 3 hrs 48 mins |
| 4 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,982,259 | 347,605 | 5.70 | 4 hrs 13 mins |
| 5 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,689,615 | 331,351 | 5.10 | 4 hrs 42 mins |
| 6 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,448,430 | 309,256 | 4.68 | 5 hrs 7 mins |
| 7 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 1,356,068 | 297,492 | 4.56 | 5 hrs 16 mins |
| 8 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,321,090 | 301,072 | 4.39 | 5 hrs 28 mins |
| 9 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,315,194 | 302,603 | 4.35 | 5 hrs 31 mins |
| 10 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,302,360 | 297,857 | 4.37 | 5 hrs 29 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,191,216 | 303,667 | 3.92 | 6 hrs 7 mins |
| 12 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 1,159,667 | 303,833 | 3.82 | 6 hrs 17 mins |
| 13 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 884,179 | 265,837 | 3.33 | 7 hrs 13 mins |
| 14 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 878,539 | 264,902 | 3.32 | 7 hrs 14 mins |
| 15 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 869,242 | 254,673 | 3.41 | 7 hrs 2 mins |
| 16 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 733,503 | 235,484 | 3.11 | 7 hrs 42 mins |
| 17 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 646,525 | 222,373 | 2.91 | 8 hrs 15 mins |
| 18 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 581,277 | 238,156 | 2.44 | 9 hrs 50 mins |
| 19 | GeForce GTX 1060 Mobile GP106M [GeForce GTX 1060 Mobile] |
Nvidia | GP106M | 498,335 | 142,988 | 3.49 | 6 hrs 53 mins |
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| 20 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 449,377 | 211,621 | 2.12 | 11 hrs 18 mins |
| 21 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 323,989 | 187,405 | 1.73 | 13 hrs 53 mins |
| 22 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 312,453 | 209,306 | 1.49 | 16 hrs 5 mins |
| 23 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 234,501 | 170,728 | 1.37 | 17 hrs 28 mins |
| 24 | RX 5500/5500M/Pro 5500M Navi 14 [RX 5500/5500M/Pro 5500M] |
AMD | Navi 14 | 181,338 | 145,827 | 1.24 | 19 hrs 18 mins |
| 25 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 102,992 | 120,663 | 0.85 | 28 hrs 7 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:52|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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