RESEARCH: DUD-E
FOLDING PROJECT #12208 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 105,529
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project simulates how proteins interact with small molecules, like drugs. They use a well-known dataset called DUD-E to test different simulation methods. This helps researchers understand how drugs work and develop new ones faster and cheaper. One example protein is Acetylcholinesterase, which is important for the nervous system and is targeted by pesticides and some medications.

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.
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RELATED TERMS GLOSSARY AI BETA

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

DUD-E

Dataset of protein-ligand interactions

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a widely used dataset for benchmarking methods that predict how proteins bind to small molecules. It contains diverse proteins and many small molecules with known binding abilities.


Protein-Ligand Interactions

The binding of a protein to a small molecule (ligand)

Scientific: Biotechnology, Pharmacology
Drug Discovery / Structural Biology

Protein-ligand interactions are essential for many biological processes. In drug discovery, understanding how proteins bind to drugs is crucial for developing new therapies.


Drug Discovery

The process of identifying and developing new drugs

Industry: Pharmaceuticals
Pharmaceutical Industry / Research and Development

Drug discovery is a complex and lengthy process that involves identifying potential drug candidates, testing their efficacy and safety, and eventually bringing them to market.


Alpha Fold

An AI system for predicting protein structures

Technical: Biotechnology
Bioinformatics / Protein Structure Prediction

AlphaFold is a groundbreaking AI system developed by DeepMind that can accurately predict the 3D structure of proteins. This has revolutionized our understanding of protein function and has immense potential for drug discovery and other applications.


Folding@Home

Distributed computing project for protein folding simulations

Technical: Biotechnology
Computational Biology / Protein Simulations

Folding@Home is a volunteer computing project that uses donated computer power to simulate protein folding. This helps researchers understand how proteins fold into their complex 3D structures, which is essential for many biological processes.


Acetylcholinesterase

An enzyme that breaks down acetylcholine

Technical: Biotechnology, Pharmaceuticals
Neuroscience / Enzyme Biology

Acetylcholinesterase is an important enzyme that plays a role in nerve impulse transmission. It breaks down the neurotransmitter acetylcholine, which is essential for muscle contraction and other functions.


ACES

Acetylcholinesterase enzyme

Technical: Biotechnology, Pharmaceuticals
Neuroscience / Target Identification

ACES refers to Acetylcholinesterase, an enzyme targeted by many drugs and pesticides due to its role in nerve function.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:32
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 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 10,197,953 329,253 30.97 0 hrs 46 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 8,935,063 331,326 26.97 0 hrs 53 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,909,582 323,162 24.48 0 hrs 59 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 7,702,305 314,836 24.46 0 hrs 59 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,396,049 295,240 21.66 1 hrs 6 mins
6 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,837,570 289,061 20.19 1 hrs 11 mins
7 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,568,516 277,632 20.06 1 hrs 12 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,624,068 265,362 17.43 1 hrs 23 mins
9 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,068,632 254,014 16.02 1 hrs 30 mins
10 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,796,204 250,236 15.17 1 hrs 35 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,172,532 236,059 13.44 1 hrs 47 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,990,235 230,019 13.00 1 hrs 51 mins
13 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,918,779 228,713 12.76 1 hrs 53 mins
14 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,885,707 228,606 12.62 1 hrs 54 mins
15 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,714,599 223,253 12.16 1 hrs 58 mins
16 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,649,853 224,051 11.83 2 hrs 2 mins
17 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,648,638 218,312 12.13 1 hrs 59 mins
18 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,522,039 218,370 11.55 2 hrs 5 mins
19 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,496,093 216,174 11.55 2 hrs 5 mins
20 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,407,651 211,637 11.38 2 hrs 7 mins
21 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,210,488 209,817 10.54 2 hrs 17 mins
22 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,085,990 205,435 10.15 2 hrs 22 mins
23 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,920,293 201,086 9.55 2 hrs 31 mins
24 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,868,948 197,413 9.47 2 hrs 32 mins
25 Quadro RTX 5000 Mobile / Max-Q
TU104GLM [Quadro RTX 5000 Mobile / Max-Q]
Nvidia TU104GLM 1,835,608 196,353 9.35 2 hrs 34 mins
26 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,599,975 184,166 8.69 2 hrs 46 mins
27 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,569,704 181,396 8.65 2 hrs 46 mins
28 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,516,559 183,354 8.27 2 hrs 54 mins
29 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,195,984 170,849 7.00 3 hrs 26 mins
30 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,056,598 166,385 6.35 3 hrs 47 mins
31 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,022,014 132,703 7.70 3 hrs 7 mins
32 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 904,543 152,934 5.91 4 hrs 3 mins
33 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 778,057 147,663 5.27 4 hrs 33 mins
34 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 718,975 150,150 4.79 5 hrs 1 mins
35 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 632,940 138,496 4.57 5 hrs 15 mins
36 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 486,882 81,272 5.99 4 hrs 0 mins
37 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 392,647 113,525 3.46 6 hrs 56 mins
38 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 233,224 99,084 2.35 10 hrs 12 mins
39 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 201,500 95,963 2.10 11 hrs 26 mins
40 Radeon 540/540X/550/550X/RX 540X/550/550X
Lexa PRO [Radeon 540/540X/550/550X/RX 540X/550/550X]
AMD Lexa PRO 54,180 64,002 0.85 28 hrs 21 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:32
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make