RESEARCH: DUD-E
FOLDING PROJECT #12208 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 105,529Core: 0x22
Status: Public
Related Projects
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
DUD-E
Dataset of 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)
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
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
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
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
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
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 |
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|
|||||||
| 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 |
|
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|||||||
| 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 |
|---|