RESEARCH: DUD-E
FOLDING PROJECT #12203 PROFILE

PROJECT TEAM

Manager(s): Louis Smith
Institution: University of Pennsylvania

WORK UNIT INFO

Atoms: 76,500
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how proteins interact with small molecules (like drugs). They're using a well-known dataset called DUD-E, which has lots of different proteins and their interactions. This helps researchers develop better methods for designing new drugs more quickly and cheaply.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

In this series of projects we are simulating proteins that are part of the DUD-E benchmark data set for protein-ligand interactions, using simulations initialized from Alpha Fold. Simulation methods to study protein-small molecule interactions are of critical importance to the early stages of drug discovery, but most methods have a poor balance of accuracy relative to cost.

Much of the development process for new compounds happens via screening large libraries of compounds for activity against target proteins believed to be relevant for a disease.

Lending focus to this search makes developing new molecules into drugs more economical and faster. In order to do this kind of methods development, good reference data that is widely available is essential.

A classic dataset for benchmarking structural methods attempting to predict protein-ligand interactions known as DUD-E has been widely used because it has diverse proteins, and each protein is bound to a fairly large collection (usually more than fifty) of small molecules for which the ability to bind the receptor have been measured experimentally.

Using Folding@Home, we will create large reference quality simulations of these proteins.

Because we know how such simulations, and the binding methods we or others may test on them, should look and function we have a great yardstick for improving the methods we have and developing new ones. In this project series we have the following systems, many of which are known for their medical relevance in addition to having been extensively studied with both simulation and experiment in the past. 12201 - ACES: Acetylcholinesterase that is critical to nervous system function in animals.

It is the target of pesticides, and also numerous drugs.

If targeted in the correct way, it can reduce neural swelling.

This sequence happens to be from the Pacific Electric ray, Torpedo Californica, which was a landmark discovery in biomedical efforts to isolate neurotransmitter receptors and led to a mechanistic understanding of myasthenia gravis. 12202 - AKT2: serine-threonine kinase taking part in the insulin signal transduction pathway.

Implicated in some cancers, it has been a target of drug development campaigns in the past. 12203 - AMPC: A critical antibiotic resistance gene, it is a beta lactamase capable of opening the critical structural feature of celphalosporin-type antibiotics, rendering them ineffective. 12204 - BACE1: Beta secretase 1, an aspartic acid protease that helps form myelin sheaths in neurons.

It is the major generator of amyloid-beta peptides in neurons, and therefore is implicated in Alzheimer's disease. 12205 - BRAF: B-raf is involved in sending signals involved in cell growth, and as such is considered a proto-oncogen.

It is a serine/threonine kinase that has several known inhibitors, some of which are now anti-cancer medications. 12206 - CASP3: a caspase-type protease that participates in the execution of apoptosis, the process of programmed cell death.

It also acts to cleave one of the amyloid forming proteins and is therefore implicated in Alzheimer's dementia. 12207 - CDK2: one of the cyclin dependent kinases, this protein is a checkpoint kinase that signals transitions between growth and DNA synthesis phases in the cell cycle.

Dysfunction in this checkpoint is associated with cancer; inhibiting CDK2 can arrest cell cycle in cases of abmormal growth, so it has been an anti-cancer target for some time. 12208 - CSF1R: Colony stimulating factor 1 receptor, when bound by cognate ligands, will promote survival, proliferation and differentiation of many myeloid cell types.

It is thus involved in disease and is targeted in therapies for cancer, neurodegenerative diseases, nad inflammatory bone diseases. 12209 - DPP4: Dipeptidyl peptidase-4, a protein that cuts up certain other proteins on the surfaces of most cells.

Important in immune regulation, signal transduction, and apoptosis, molecules inhibiting its enzymatic activity can help treat type 2 diabetes because the peptide hormones (GLP-1, and GIP) are degraded by DPP4.

Thus, inhibiting DPP4 prolongs the effects of these hormones. 12210 - EGFR: Epidermal growth factor receptor; its deficient signaling is associated with Alzheimer's dementia, whereas its over-expression is a common characteristic of tumor cells.

It is thus an oncogene that is targeted by numerous anti-cancer molecules and drugs.

Many of these are targeted at the tyrosine kinase domain, because hampering its function prevents excessive transduction of the signals these receptors would otherwise send to the nucleus of the tumor cell. 12211 - ESR1: Estrogen Receptor Alpha is critical to many tissue differentiation processes across the body, and has been targeted by various drugs to both enhance and suppress its effects depending on associated conditions.

12212 - FA10: Coagulation factor X is an enzyme in the coagulation signaling cascade for forming blood clots.

It is a serine endopeptidase, and has been targeted by inhibitors to reduce coagulation in medical contexts where that is desirable.
.

RELATED TERMS GLOSSARY AI BETA

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

DUD-E

Dataset for benchmarking protein-ligand interactions

Technical: Pharmaceutical
Biotechnology / Drug Discovery

DUD-E is a widely used dataset in the field of drug discovery. It contains information about various proteins and their interactions with small molecules, which helps researchers evaluate the accuracy of computational methods used to predict these interactions.


Alpha Fold

AI system for predicting protein structure

Technical: Pharmaceutical
Biotechnology / Computational Biology

AlphaFold is a powerful artificial intelligence program developed by DeepMind that can accurately predict the three-dimensional structure of proteins. This breakthrough has revolutionized our understanding of protein function and has vast implications for drug discovery and other areas of biotechnology.


Protein-Ligand Interactions

Binding between a protein and a small molecule (ligand)

Scientific: Pharmaceutical
Biotechnology / Drug Discovery

Protein-ligand interactions are essential for many biological processes. In drug discovery, understanding these interactions is crucial as drugs often work by binding to specific proteins.


Drug Discovery

Process of identifying and developing new drugs

Process: Pharmaceutical
Biotechnology / Pharmaceutical Research

Drug discovery is a complex and lengthy process involving numerous steps, from identifying potential drug targets to testing and manufacturing new medications. It requires expertise in various fields, including biology, chemistry, pharmacology, and medicine.


Acetylcholinesterase

Enzyme that breaks down acetylcholine in the nervous system

Technical: Pharmaceutical
Biotechnology / Neurobiology

Acetylcholinesterase is an enzyme that plays a vital role in nerve impulse transmission. It breaks down acetylcholine, a neurotransmitter, after it has transmitted its signal. This process is essential for proper muscle function and cognitive processes.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:35
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 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 10,312,986 231,716 44.51 0 hrs 32 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 10,219,743 228,839 44.66 0 hrs 32 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,402,687 207,748 35.63 0 hrs 40 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,606,726 199,396 33.13 0 hrs 43 mins
5 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,144,461 195,369 31.45 0 hrs 46 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,528,771 175,949 25.74 0 hrs 56 mins
7 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 4,482,235 176,373 25.41 0 hrs 57 mins
8 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 4,398,135 173,733 25.32 0 hrs 57 mins
9 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,010,449 169,069 23.72 1 hrs 1 mins
10 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,701,375 165,796 22.32 1 hrs 5 mins
11 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,228,262 157,354 20.52 1 hrs 10 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,203,234 157,360 20.36 1 hrs 11 mins
13 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,182,722 157,356 20.23 1 hrs 11 mins
14 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,808,008 150,227 18.69 1 hrs 17 mins
15 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,378,813 143,091 16.62 1 hrs 27 mins
16 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,297,943 140,717 16.33 1 hrs 28 mins
17 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,174,354 131,394 16.55 1 hrs 27 mins
18 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,078,541 136,104 15.27 1 hrs 34 mins
19 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,065,005 136,220 15.16 1 hrs 35 mins
20 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,038,754 135,097 15.09 1 hrs 35 mins
21 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,917,114 133,239 14.39 1 hrs 40 mins
22 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,808,981 130,036 13.91 1 hrs 44 mins
23 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,474,560 121,608 12.13 1 hrs 59 mins
24 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,293,158 116,588 11.09 2 hrs 10 mins
25 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,096,609 109,879 9.98 2 hrs 24 mins
26 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,050,029 99,749 10.53 2 hrs 17 mins
27 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 965,002 102,379 9.43 2 hrs 33 mins
28 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 871,595 90,737 9.61 2 hrs 30 mins
29 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 733,012 97,432 7.52 3 hrs 11 mins
30 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 585,883 89,313 6.56 3 hrs 40 mins
31 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 571,476 88,787 6.44 3 hrs 44 mins
32 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 500,076 80,721 6.20 3 hrs 52 mins
33 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 400,038 78,933 5.07 4 hrs 44 mins
34 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 393,186 78,166 5.03 4 hrs 46 mins
35 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 363,687 76,367 4.76 5 hrs 2 mins
36 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 339,136 74,339 4.56 5 hrs 16 mins
37 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 230,117 65,464 3.52 6 hrs 50 mins
38 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 119,040 57,558 2.07 11 hrs 36 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

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