RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12110 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 3,900Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project relates to figuring out how small pieces of protein (peptides) fold into different shapes. These shapes are important because they determine what the peptides can do. By using computer models and machine learning, scientists hope to understand all the possible shapes that peptides can take. This knowledge can then be used to design new peptides for use in things like bio-inspired 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.
A peptide is a small molecule made up of several amino acids joined together. They are important building blocks in proteins and have various functions in the body, such as signaling and transporting molecules.
Conformations
The different 3D shapes a molecule can adopt.
Conformations refer to the various three-dimensional shapes that a molecule can take. These shapes are important because they influence how a molecule interacts with other molecules and performs its function.
Simulations
Computer-based models that mimic real-world processes.
Simulations are computer programs that imitate complex systems and processes. They are used in many fields, including science and engineering, to study and predict how things work.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning is a subset of artificial intelligence where computers are trained on large datasets to identify patterns and make predictions. It's used in various applications, including image recognition, speech synthesis, and drug discovery.
Rational Design
The process of designing molecules with specific properties.
Rational design is a systematic approach to creating new molecules with desired properties. It involves understanding the structure-function relationships of target molecules and using this knowledge to design novel compounds.
Biologically-Inspired Materials
Materials that are designed based on biological structures or processes.
Biologically-inspired materials draw inspiration from nature's designs and functionalities. These materials aim to mimic the properties of biological systems, such as strength, flexibility, and self-healing capabilities.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:51|
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 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 5,848,552 | 66,640 | 87.76 | 0 hrs 16 mins |
| 2 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 5,715,977 | 65,721 | 86.97 | 0 hrs 17 mins |
| 3 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 5,184,000 | 6,000 | 864.00 | 0 hrs 2 mins |
| 4 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 5,163,683 | 25,759 | 200.46 | 0 hrs 7 mins |
| 5 | TITAN X GP102 [TITAN X] 6144 |
Nvidia | GP102 | 4,357,293 | 61,614 | 70.72 | 0 hrs 20 mins |
| 6 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 4,342,519 | 61,841 | 70.22 | 0 hrs 21 mins |
| 7 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 4,215,936 | 59,565 | 70.78 | 0 hrs 20 mins |
| 8 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 4,082,169 | 56,648 | 72.06 | 0 hrs 20 mins |
| 9 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 3,858,605 | 58,747 | 65.68 | 0 hrs 22 mins |
| 10 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 3,311,461 | 55,698 | 59.45 | 0 hrs 24 mins |
| 11 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 2,879,914 | 52,158 | 55.22 | 0 hrs 26 mins |
| 12 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 2,227,497 | 46,146 | 48.27 | 0 hrs 30 mins |
| 13 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 1,728,598 | 44,510 | 38.84 | 0 hrs 37 mins |
| 14 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 1,355,586 | 6,000 | 225.93 | 0 hrs 6 mins |
| 15 | Radeon 660M-680M Rembrandt [Radeon 660M-680M] |
AMD | Rembrandt | 915,947 | 36,831 | 24.87 | 0 hrs 58 mins |
| 16 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 617,760 | 6,000 | 102.96 | 0 hrs 14 mins |
| 17 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 290,310 | 23,361 | 12.43 | 1 hrs 56 mins |
| 18 | Vega Mobile 5000 series APU Cezanne [Vega Mobile 5000 series APU] |
AMD | Cezanne | 209,758 | 13,863 | 15.13 | 1 hrs 35 mins |
| 19 | Radeon R9 285/380 Tonga PRO [Radeon R9 285/380] |
AMD | Tonga PRO | 190,684 | 21,526 | 8.86 | 2 hrs 43 mins |
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| 20 | R7 370/R9 270/370 OEM Curacao Pro [R7 370/R9 270/370 OEM] |
AMD | Curacao Pro | 174,898 | 19,014 | 9.20 | 2 hrs 37 mins |
| 21 | Ryzen 7000 Series iGPU Raphael [Ryzen 7000 Series iGPU] |
AMD | Raphael | 132,026 | 18,317 | 7.21 | 3 hrs 20 mins |
| 22 | R7 370/R9 270X/370X Curacao XT/Trinidad XT [R7 370/R9 270X/370X] |
AMD | Curacao XT/Trinidad XT | 124,081 | 18,794 | 6.60 | 3 hrs 38 mins |
| 23 | HD 7850/R7 265/R9 270 1024SP Pitcairn PRO [HD 7850/R7 265/R9 270 1024SP] |
AMD | Pitcairn PRO | 114,196 | 17,371 | 6.57 | 3 hrs 39 mins |
| 24 | R9 280X/HD 7900/8970 OEM Tahiti XT [R9 280X/HD 7900/8970 OEM] |
AMD | Tahiti XT | 69,456 | 15,318 | 4.53 | 5 hrs 18 mins |
| 25 | Radeon 540/540X/550/550X/RX 540X/550/550X Lexa PRO [Radeon 540/540X/550/550X/RX 540X/550/550X] |
AMD | Lexa PRO | 61,433 | 14,930 | 4.11 | 5 hrs 50 mins |
| 26 | R7 240/340/520/HD8570 Hawaii [R7 240/340/520/HD8570] |
AMD | Hawaii | 25,298 | 11,051 | 2.29 | 10 hrs 29 mins |
| 27 | Ryzen 4900HS mobile Renoir [Ryzen 4900HS mobile] |
AMD | Renoir | 21,777 | 9,077 | 2.40 | 10 hrs 0 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:51|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|