RESEARCH: DUD-E
FOLDING PROJECT #12205 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 77,423Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project uses computer simulations to study how proteins interact with small molecules, like drugs. They're focusing on a set of proteins called DUD-E, which are important for health and have been studied a lot. By simulating these interactions accurately, they hope to improve drug discovery methods and develop new treatments.
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
Directory of Useful Decoys - Enhanced
DUD-E is a benchmark dataset used to evaluate the accuracy of computational methods for predicting protein-ligand interactions. It contains diverse proteins with experimentally determined binding affinities for numerous small molecules.
Protein-Ligand Interactions
The binding of a protein to a small molecule ligand, often leading to biological effects.
Protein-ligand interactions are essential for many biological processes. In drug discovery, understanding how drugs bind to target proteins is crucial for developing new therapies.
Drug Discovery
The process of identifying and developing new drugs.
Drug discovery is a complex and multi-stage process that involves identifying potential drug candidates, testing their efficacy and safety, and ultimately bringing them to market.
Alpha Fold
A deep learning algorithm for predicting protein structures.
AlphaFold is a revolutionary AI system that can predict the 3D structure of proteins with remarkable accuracy. This has immense implications for understanding protein function and designing new drugs.
Folding@Home
A distributed computing project for simulating protein folding.
Folding@Home harnesses the power of volunteer computer resources to simulate protein folding and other complex biological processes.
Acetylcholinesterase
An enzyme that breaks down acetylcholine in the nervous system.
Acetylcholinesterase plays a crucial role in nerve impulse transmission by regulating the levels of acetylcholine, a neurotransmitter.
Pesticides
Chemicals used to control pests.
Pesticides are widely used in agriculture to protect crops from insects, weeds, and other pests. However, they can also have negative impacts on the environment and human health.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:34|
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 | 9,896,770 | 225,584 | 43.87 | 0 hrs 33 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 8,540,984 | 210,586 | 40.56 | 0 hrs 36 mins |
| 3 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 7,245,918 | 207,354 | 34.94 | 0 hrs 41 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,573,998 | 197,114 | 33.35 | 0 hrs 43 mins |
| 5 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 5,507,438 | 192,129 | 28.67 | 0 hrs 50 mins |
| 6 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,964,295 | 181,689 | 27.32 | 0 hrs 53 mins |
| 7 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,288,593 | 167,850 | 25.55 | 0 hrs 56 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,156,397 | 171,707 | 24.21 | 0 hrs 59 mins |
| 9 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,699,949 | 166,336 | 22.24 | 1 hrs 5 mins |
| 10 | Radeon RX 7900XT/XTX Navi 31 [Radeon RX 7900XT/XTX] |
AMD | Navi 31 | 3,107,914 | 156,946 | 19.80 | 1 hrs 13 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,101,483 | 155,874 | 19.90 | 1 hrs 12 mins |
| 12 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,026,703 | 154,198 | 19.63 | 1 hrs 13 mins |
| 13 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,024,317 | 152,712 | 19.80 | 1 hrs 13 mins |
| 14 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,685,805 | 146,341 | 18.35 | 1 hrs 18 mins |
| 15 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,368,477 | 142,621 | 16.61 | 1 hrs 27 mins |
| 16 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,298,456 | 137,676 | 16.69 | 1 hrs 26 mins |
| 17 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,036,112 | 132,705 | 15.34 | 1 hrs 34 mins |
| 18 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,023,934 | 134,903 | 15.00 | 1 hrs 36 mins |
| 19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,976,269 | 133,802 | 14.77 | 1 hrs 37 mins |
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|||||||
| 20 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,833,398 | 131,311 | 13.96 | 1 hrs 43 mins |
| 21 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,269,782 | 115,607 | 10.98 | 2 hrs 11 mins |
| 22 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,209,436 | 113,615 | 10.65 | 2 hrs 15 mins |
| 23 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,206,865 | 113,881 | 10.60 | 2 hrs 16 mins |
| 24 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,159,474 | 101,651 | 11.41 | 2 hrs 6 mins |
| 25 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,004,870 | 101,808 | 9.87 | 2 hrs 26 mins |
| 26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 990,302 | 105,736 | 9.37 | 2 hrs 34 mins |
| 27 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 980,748 | 106,262 | 9.23 | 2 hrs 36 mins |
| 28 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 862,137 | 101,480 | 8.50 | 2 hrs 49 mins |
| 29 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 833,465 | 100,467 | 8.30 | 2 hrs 54 mins |
| 30 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 743,884 | 85,909 | 8.66 | 2 hrs 46 mins |
| 31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 603,914 | 90,376 | 6.68 | 3 hrs 35 mins |
| 32 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 563,537 | 88,606 | 6.36 | 3 hrs 46 mins |
| 33 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 506,457 | 86,718 | 5.84 | 4 hrs 7 mins |
| 34 | Ryzen 7000 Series iGPU Raphael [Ryzen 7000 Series iGPU] |
AMD | Raphael | 438,449 | 32,476 | 13.50 | 1 hrs 47 mins |
| 35 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 404,865 | 78,131 | 5.18 | 4 hrs 38 mins |
| 36 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 397,542 | 78,498 | 5.06 | 4 hrs 44 mins |
| 37 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 355,220 | 75,982 | 4.68 | 5 hrs 8 mins |
| 38 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 228,600 | 66,482 | 3.44 | 6 hrs 59 mins |
| 39 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 114,667 | 51,967 | 2.21 | 10 hrs 53 mins |
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|||||||
| 40 | Quadro M2000 GM206GL [Quadro M2000] |
Nvidia | GM206GL | 112,362 | 57,368 | 1.96 | 12 hrs 15 mins |
| 41 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 73,782 | 30,688 | 2.40 | 9 hrs 59 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:34|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|