RESEARCH: DUD-E
FOLDING PROJECT #12203 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 76,500Core: 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 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.
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RELATED TERMS GLOSSARY AI BETA
DUD-E
Dataset for benchmarking protein-ligand interactions
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
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)
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
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
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 |
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|
|||||||
| 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 |
|---|