RESEARCH: DUD-E
FOLDING PROJECT #12207 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 82,887Core: 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 proteins from the DUD-E dataset, which is a collection of well-studied proteins and their interactions with different compounds. By simulating these interactions, researchers can better understand how drugs work and develop new, more effective 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 widely used dataset in drug discovery research. It contains diverse proteins bound to various small molecules, allowing researchers to benchmark and improve methods for predicting protein-ligand interactions.
protein-ligand interactions
The binding of a protein and a ligand (e.g., drug molecule) to each other.
Protein-ligand interactions are essential for many biological processes. In drug discovery, researchers study these interactions to understand how drugs bind to target proteins and exert their effects.
drug discovery
The process of identifying and developing new drugs.
Drug discovery is a complex process that involves multiple stages, from identifying potential drug targets to testing and manufacturing new medications. It aims to develop safe and effective treatments for diseases.
Alpha Fold
A deep learning algorithm that predicts the 3D structure of proteins.
AlphaFold is a revolutionary AI system that accurately predicts protein structures from amino acid sequences. This breakthrough has immense implications for understanding biological processes and developing new drugs.
Folding@Home
A distributed computing project that uses volunteers' computers to simulate protein folding.
Folding@Home harnesses the power of many computers to perform complex simulations of protein folding. This collaborative effort accelerates research in various fields, including drug discovery and disease understanding.
Acetylcholinesterase
An enzyme that breaks down acetylcholine, a neurotransmitter.
Acetylcholinesterase is crucial for regulating nerve impulses. It controls the breakdown of acetylcholine, ensuring proper communication between nerve cells. Medications targeting this enzyme are used to treat conditions like Alzheimer's disease.
Torpedo Californica
The scientific name for the California electric ray.
Torpedo Californica, commonly known as the California electric ray, is a marine species known for its ability to generate electrical shocks. It has been studied extensively for its neurotransmitter receptors, contributing to advancements in biomedical research.
Neurotransmitter
A chemical messenger that transmits signals between nerve cells.
Neurotransmitters are crucial for communication within the nervous system. They allow nerve cells to transmit signals, controlling various bodily functions, emotions, and behaviors.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:33|
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 | 11,626,396 | 252,233 | 46.09 | 0 hrs 31 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 9,638,059 | 238,026 | 40.49 | 0 hrs 36 mins |
| 3 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 7,483,909 | 220,133 | 34.00 | 0 hrs 42 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,981,866 | 213,917 | 32.64 | 0 hrs 44 mins |
| 5 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,166,179 | 206,368 | 29.88 | 0 hrs 48 mins |
| 6 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,565,003 | 187,346 | 24.37 | 0 hrs 59 mins |
| 7 | Radeon RX 7900XT/XTX Navi 31 [Radeon RX 7900XT/XTX] |
AMD | Navi 31 | 3,838,525 | 176,164 | 21.79 | 1 hrs 6 mins |
| 8 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,645,124 | 172,841 | 21.09 | 1 hrs 8 mins |
| 9 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,636,581 | 172,130 | 21.13 | 1 hrs 8 mins |
| 10 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,193,205 | 166,531 | 19.17 | 1 hrs 15 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,107,393 | 163,990 | 18.95 | 1 hrs 16 mins |
| 12 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,960,130 | 160,864 | 18.40 | 1 hrs 18 mins |
| 13 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,800,753 | 158,377 | 17.68 | 1 hrs 21 mins |
| 14 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,604,500 | 156,149 | 16.68 | 1 hrs 26 mins |
| 15 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,555,443 | 154,118 | 16.58 | 1 hrs 27 mins |
| 16 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,531,668 | 149,724 | 16.91 | 1 hrs 25 mins |
| 17 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,281,624 | 146,968 | 15.52 | 1 hrs 33 mins |
| 18 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,053,655 | 143,852 | 14.28 | 1 hrs 41 mins |
| 19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,006,932 | 142,358 | 14.10 | 1 hrs 42 mins |
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|||||||
| 20 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,960,915 | 141,368 | 13.87 | 1 hrs 44 mins |
| 21 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,745,137 | 135,650 | 12.86 | 1 hrs 52 mins |
| 22 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,622,064 | 132,347 | 12.26 | 1 hrs 57 mins |
| 23 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,339,896 | 123,789 | 10.82 | 2 hrs 13 mins |
| 24 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 1,324,500 | 122,912 | 10.78 | 2 hrs 14 mins |
| 25 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,276,334 | 122,522 | 10.42 | 2 hrs 18 mins |
| 26 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,193,483 | 119,509 | 9.99 | 2 hrs 24 mins |
| 27 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,182,552 | 119,214 | 9.92 | 2 hrs 25 mins |
| 28 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,113,929 | 117,016 | 9.52 | 2 hrs 31 mins |
| 29 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,087,169 | 115,151 | 9.44 | 2 hrs 33 mins |
| 30 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 809,742 | 104,983 | 7.71 | 3 hrs 7 mins |
| 31 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 805,573 | 105,679 | 7.62 | 3 hrs 9 mins |
| 32 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 792,526 | 104,502 | 7.58 | 3 hrs 10 mins |
| 33 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 738,073 | 94,832 | 7.78 | 3 hrs 5 mins |
| 34 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 676,005 | 78,319 | 8.63 | 2 hrs 47 mins |
| 35 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 602,173 | 96,107 | 6.27 | 3 hrs 50 mins |
| 36 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 468,605 | 84,628 | 5.54 | 4 hrs 20 mins |
| 37 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 410,575 | 84,097 | 4.88 | 4 hrs 55 mins |
| 38 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 385,772 | 82,180 | 4.69 | 5 hrs 7 mins |
| 39 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 260,452 | 77,114 | 3.38 | 7 hrs 6 mins |
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| 40 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 221,787 | 68,221 | 3.25 | 7 hrs 23 mins |
| 41 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 209,021 | 65,255 | 3.20 | 7 hrs 30 mins |
| 42 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 200,048 | 65,066 | 3.07 | 7 hrs 48 mins |
| 43 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 147,603 | 59,577 | 2.48 | 9 hrs 41 mins |
| 44 | R7 370/R9 270X/370X Curacao XT/Trinidad XT [R7 370/R9 270X/370X] |
AMD | Curacao XT/Trinidad XT | 76,036 | 47,982 | 1.58 | 15 hrs 9 mins |
| 45 | Quadro P600 GP107GL [Quadro P600] |
Nvidia | GP107GL | 74,180 | 54,710 | 1.36 | 17 hrs 42 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:33|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|