RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12121 PROFILE

PROJECT TEAM

Manager(s): Hassan Nadeem
Institution: University of Illinois at Urbana-Champaign

WORK UNIT INFO

Atoms: 40,000
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project explores the different shapes short chains of amino acids (peptides) can take. Using computer models and machine learning, they aim to understand how these shapes affect peptide function. This knowledge could be used to design new peptides for use in materials inspired by nature.

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

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

Peptide

A short chain of amino acids linked together.

Scientific: Pharmaceuticals
Biotechnology / Protein Structure

Peptides are small chains of amino acids that serve various functions in living organisms. They can act as hormones, enzymes, or structural components. Understanding their structure and how they fold is crucial for developing new drugs and materials.


Conformations

The different 3-dimensional shapes a molecule can adopt.

Scientific: Pharmaceuticals
Biotechnology / Protein Structure

Conformations refer to the various shapes that molecules can take. For peptides, understanding their conformations is essential because it influences their function. Different shapes allow peptides to bind to specific targets or interact with other molecules in unique ways.


Simulations

Computer models that mimic real-world processes.

Scientific: Pharmaceuticals
Biotechnology / Computational Biology

Simulations are used in various scientific fields to study complex systems. In biotechnology, simulations can be used to predict how molecules will behave under different conditions, such as protein folding or drug interactions.


Machine Learning

A type of artificial intelligence that allows computers to learn from data.

Technical: Pharmaceuticals
Biotechnology / Data Science

Machine learning is a powerful tool used in biotechnology to analyze large datasets and identify patterns. It can be used to predict protein structures, design new drugs, or personalize treatment plans.


Rational Design

The process of designing molecules with specific properties.

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

Rational design involves using knowledge about a molecule's structure and function to create new molecules with desired characteristics. This is commonly used in drug development to create more effective and safer medications.


Biologically-Inspired Materials

Materials that are designed based on biological principles.

Scientific: Manufacturing
Materials Science / Biomimetics

Biologically-inspired materials mimic the properties of natural materials, such as strength, flexibility, or self-healing abilities. These materials have potential applications in various industries, including medicine, aerospace, and construction.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:45
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 2,841,389 176,358 16.11 1 hrs 29 mins
2 GeForce RTX 2060 12GB
TU106 [GeForce RTX 2060 12GB]
Nvidia TU106 2,453,744 167,708 14.63 1 hrs 38 mins
3 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,159,582 152,717 14.14 1 hrs 42 mins
4 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,093,196 159,136 13.15 1 hrs 49 mins
5 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,029,719 157,768 12.87 1 hrs 52 mins
6 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,009,605 154,256 13.03 1 hrs 51 mins
7 GeForce RTX 2070 Mobile / Max-Q Refresh
TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh]
Nvidia TU106M 1,790,000 151,259 11.83 2 hrs 2 mins
8 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,675,642 124,391 13.47 1 hrs 47 mins
9 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,591,162 144,952 10.98 2 hrs 11 mins
10 GeForce RTX 3050 8GB
GA107 [GeForce RTX 3050 8GB]
Nvidia GA107 1,565,909 144,724 10.82 2 hrs 13 mins
11 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,363,060 133,133 10.24 2 hrs 21 mins
12 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,320,856 136,293 9.69 2 hrs 29 mins
13 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,293,594 84,085 15.38 1 hrs 34 mins
14 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,153,274 113,572 10.15 2 hrs 22 mins
15 GeForce RTX 2070 Mobile
TU106M [GeForce RTX 2070 Mobile]
Nvidia TU106M 1,146,250 130,337 8.79 2 hrs 44 mins
16 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,096,808 82,863 13.24 1 hrs 49 mins
17 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,071,162 127,275 8.42 2 hrs 51 mins
18 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 978,858 123,671 7.92 3 hrs 2 mins
19 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 850,005 42,950 19.79 1 hrs 13 mins
20 P106-100
GP106 [P106-100]
Nvidia GP106 772,551 114,769 6.73 3 hrs 34 mins
21 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 728,833 36,145 20.16 1 hrs 11 mins
22 Quadro P3200 Mobile
GP104GLM [Quadro P3200 Mobile]
Nvidia GP104GLM 724,526 85,590 8.47 2 hrs 50 mins
23 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 694,017 110,921 6.26 3 hrs 50 mins
24 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 662,848 108,527 6.11 3 hrs 56 mins
25 GeForce GTX 1650 Ti Mobile
TU117M [GeForce GTX 1650 Ti Mobile]
Nvidia TU117M 580,231 103,928 5.58 4 hrs 18 mins
26 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 557,282 51,847 10.75 2 hrs 14 mins
27 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 513,751 36,145 14.21 1 hrs 41 mins
28 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 462,964 95,056 4.87 4 hrs 56 mins
29 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 291,552 84,706 3.44 6 hrs 58 mins
30 Quadro M4000
GM204GL [Quadro M4000]
Nvidia GM204GL 289,722 83,721 3.46 6 hrs 56 mins
31 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 278,216 36,145 7.70 3 hrs 7 mins
32 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 260,216 79,674 3.27 7 hrs 21 mins
33 GeForce GTX 1050 3 GB Max-Q
GP107M [GeForce GTX 1050 3 GB Max-Q]
Nvidia GP107M 246,482 78,707 3.13 7 hrs 40 mins
34 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 170,926 71,689 2.38 10 hrs 4 mins
35 GeForce GTX 750
GM107 [GeForce GTX 750] 1111
Nvidia GM107 112,643 64,857 1.74 13 hrs 49 mins
36 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 58,936 50,810 1.16 20 hrs 41 mins
37 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 40,706 38,146 1.07 22 hrs 29 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:45
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make