RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12103 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 41,000
Core: 0x22
Status: Public

Related Projects

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

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

Peptide

A short chain of amino acids linked together.

Scientific: Biotechnology
Biotechnology / Peptides and Proteins

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.

Scientific: Pharmaceuticals
Biotechnology / Structural Biology

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.

Scientific: Biotechnology
Biotechnology / Computational Biology

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.

Technical: Biotechnology
Biotechnology / Data Science

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.

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

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.

Scientific: Materials Science
Materials Science / Biomimetics

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
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