RESEARCH: DUD-E
FOLDING PROJECT #12209 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 120,837Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project uses computer simulations to study how medicines interact with proteins. They're focusing on a protein called ACES, which is important for the nervous system and is also targeted by pesticides and some drugs. By understanding how medicines bind to ACES, scientists can develop new and better 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.
.
RELATED TERMS GLOSSARY AI BETA
DUD-E
Directory of Useful Drugs - Experiments
DUD-E is a benchmark dataset used in drug discovery research. It contains information about protein-ligand interactions, which are important for understanding how drugs work.
Protein-Ligand Interactions
The binding of a protein to a small molecule ligand.
Protein-ligand interactions are essential for many biological processes, including drug action. A protein can bind to a small molecule (a ligand) in a specific way, leading to changes in the protein's function.
Alpha Fold
An artificial intelligence system for predicting protein structures.
AlphaFold is a powerful AI tool that can predict the 3D structure of proteins. This is crucial for understanding how proteins function and for designing new drugs.
Drug Discovery
The process of identifying and developing new drugs.
Drug discovery is a complex process that involves many steps, from identifying potential drug targets to testing and manufacturing new medications. It aims to develop safe and effective treatments for diseases.
Pesticides
Chemicals used to kill pests.
Pesticides are widely used in agriculture to protect crops from damage by insects, weeds, and other organisms. However, their use can have negative impacts on the environment and human health.
Acetylcholinesterase
An enzyme that breaks down the neurotransmitter acetylcholine.
Acetylcholinesterase is a crucial enzyme in the nervous system. It helps to regulate nerve impulses by breaking down acetylcholine, a neurotransmitter involved in muscle contraction and other functions.
Neurotransmitter
A chemical messenger that transmits signals between nerve cells.
Neurotransmitters are essential for communication within the nervous system. They allow nerve cells to send and receive signals, which control various bodily functions, including movement, thought, and emotion.
Folding@Home
A distributed computing project that uses volunteers' computer resources to simulate protein folding.
Folding@Home harnesses the power of many computers to simulate how proteins fold into their 3D structures. This is important for understanding how proteins function and for designing new drugs.
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 | 12,753,254 | 422,525 | 30.18 | 0 hrs 48 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 10,263,354 | 397,883 | 25.79 | 0 hrs 56 mins |
| 3 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 8,280,654 | 369,651 | 22.40 | 1 hrs 4 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 8,140,016 | 363,961 | 22.37 | 1 hrs 4 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,029,986 | 348,700 | 20.16 | 1 hrs 11 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,657,676 | 343,011 | 19.41 | 1 hrs 14 mins |
| 7 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,471,573 | 341,914 | 18.93 | 1 hrs 16 mins |
| 8 | Radeon RX 7900XT/XTX Navi 31 [Radeon RX 7900XT/XTX] |
AMD | Navi 31 | 5,746,583 | 328,037 | 17.52 | 1 hrs 22 mins |
| 9 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,948,348 | 312,689 | 15.83 | 1 hrs 31 mins |
| 10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,379,047 | 297,473 | 14.72 | 1 hrs 38 mins |
| 11 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 4,002,986 | 292,235 | 13.70 | 1 hrs 45 mins |
| 12 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,985,327 | 290,882 | 13.70 | 1 hrs 45 mins |
| 13 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,412,836 | 276,122 | 12.36 | 1 hrs 57 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,268,315 | 271,913 | 12.02 | 1 hrs 60 mins |
| 15 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,038,827 | 265,768 | 11.43 | 2 hrs 6 mins |
| 16 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,981,522 | 263,768 | 11.30 | 2 hrs 7 mins |
| 17 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,957,248 | 264,151 | 11.20 | 2 hrs 9 mins |
| 18 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,901,058 | 261,920 | 11.08 | 2 hrs 10 mins |
| 19 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,859,209 | 260,282 | 10.99 | 2 hrs 11 mins |
|
|
|||||||
| 20 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,822,451 | 259,004 | 10.90 | 2 hrs 12 mins |
| 21 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,467,539 | 243,158 | 10.15 | 2 hrs 22 mins |
| 22 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,338,874 | 242,147 | 9.66 | 2 hrs 29 mins |
| 23 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,175,936 | 233,122 | 9.33 | 2 hrs 34 mins |
| 24 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 2,011,223 | 231,963 | 8.67 | 2 hrs 46 mins |
| 25 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,996,034 | 229,016 | 8.72 | 2 hrs 45 mins |
| 26 | Quadro RTX 5000 Mobile / Max-Q TU104GLM [Quadro RTX 5000 Mobile / Max-Q] |
Nvidia | TU104GLM | 1,893,731 | 225,797 | 8.39 | 2 hrs 52 mins |
| 27 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,821,533 | 222,619 | 8.18 | 2 hrs 56 mins |
| 28 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,625,986 | 155,761 | 10.44 | 2 hrs 18 mins |
| 29 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,435,225 | 206,691 | 6.94 | 3 hrs 27 mins |
| 30 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,275,209 | 200,457 | 6.36 | 3 hrs 46 mins |
| 31 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,188,756 | 193,533 | 6.14 | 3 hrs 54 mins |
| 32 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,131,622 | 191,352 | 5.91 | 4 hrs 3 mins |
| 33 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,025,292 | 185,138 | 5.54 | 4 hrs 20 mins |
| 34 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 668,286 | 160,372 | 4.17 | 5 hrs 46 mins |
| 35 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 524,570 | 149,420 | 3.51 | 6 hrs 50 mins |
| 36 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 457,275 | 121,001 | 3.78 | 6 hrs 21 mins |
| 37 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 418,251 | 141,828 | 2.95 | 8 hrs 8 mins |
| 38 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 403,075 | 135,681 | 2.97 | 8 hrs 5 mins |
| 39 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 245,038 | 129,645 | 1.89 | 12 hrs 42 mins |
|
|
|||||||
| 40 | Quadro K2000 GK107 [Quadro K2000] |
Nvidia | GK107 | 12,968 | 40,819 | 0.32 | 75 hrs 33 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 |
|---|