RESEARCH: DUD-E
FOLDING PROJECT #12234 PROFILE

PROJECT TEAM

Manager(s): Louis Smith
Institution: University of Pennsylvania

WORK UNIT INFO

Atoms: 95,268
Core: 0x23
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how drugs interact with proteins. They're focusing on a protein called acetylcholinesterase, which is important for the nervous system and is targeted by pesticides and some medications. By creating accurate simulations, 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. 12234 - 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. 12235 - 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. 12236 - 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. 12237 - 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. 12238 - 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. 12239 - 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. 1240 - 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. 12241 - 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. 12242 - 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. 12243 - 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. 12244 - 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.

12245 - 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.
12246 - FABP4: Fatty Acid Binding protein 4 is a protein that imports lipids between intra and extracellular membranes in macrophages and adipocytes.

Inhibiting it is associated with both preventing certain fat-tumor cancers and metabolic syndromes. 12247 - GRIA2: Glutamate ionotropic receptor AMPA type subunit 2 is a glutamate receptor, an essential neurotransmitter in humans.

Its pre-mRNA is A->I edited at a particular site that makes the channel impermeable to calcium.

Editing errors here can result in ALS, and some other diseases. 12248 - HSP90AA1: Heat shock protein 90kDa alpha A1 is a stress inducible protein that refolds misfolded or damaged proteins.

It is a relevant drug target because it interacts with a number of tumor promoting proteins and plays a large role in cellular adaptation to stress. 12249 - IGF1R: Insulin like growth factor 1 is an extracellular receptor with a tyrosine kinase domain.

It is critical for growth and development, and as such if overproduced can contribute to the cancer phenotype and certain other diseases.

Therefore inhibitors have been developed to target this extracellular receptor. 12250 - ITAL (LFA-1): Leukocyte adhesion cglycoprotein LFA-1 alpha is an integrin found on lymphocytes and other leukocytes.

It functions in the process of tissue emigration in lymphocytes, and in cytotoxic T-cell mediated killing of cells. 12251 - KIT: tyrosine-protein kinase KIT is a receptor tyrosine kinase and a proto-oncogene.

It senses cytokines, transducing signals that govern cell proliferation and survival.

As such it is often mutated in cancers, where its excessive activity maintains or enhances the tumor state. 12252 - MAPK2: Mitogen-activated protein kinase kinase is part of the MAPK pathway, which is famously aberrant in many types of cancers, particularly melanomas.

Inhibitors against it would slow the progression of cancer, so it has been targeted by therapies historically. 12253 - MET: tyrosine-protein kinase Met, also known as hepatocyte growth factor receptor, governs embryonic development, organogenesis and wound-healing.

Abnormal activation of MET sustains tumors by causing them to grow and become better supplied by blood vessels.

Extensive research has focused on inhibiting MET because of its correlation with poor prognosis in cancer, and many compounds are in various parts of the regulatory approval process. 12254 - MK10: MAPK-10 or mitogen-activated protein kinase 10 is associated with a wide variety of cellular processes associated with proliferatiation, differentiation, and development.

Mapk-10 is implicated in neuronal development, and when active can inhibit neuronal apoptosis.

12255 - MK14: MAPK-14 or p38-alpha is another stress and differentiation controlling kinase.

Because of its interaction with inflammatory signaling in the immune system, it is a relevant target for immune diseases and heart disease. 12256 - PPARD: Peroxisome proliferator-activated receptor delta is a nuclear hormone receptor that is implicated in the development of several classes of chronic disease.

Drugs stimulating it can act as biochemical substitutes for exercise, and decouple oxidative phosphorylation.

12257 - PPARG: Peroxisome proliferator activated receptor gamma is similar to the delta variant in some ways, but also serves as a master-regulator of fat cell differentiation.

It has been studied as a target for growth inhibition in cancer cell cultures.

It also is targeted by drugs that treat lipid metabolism disorders like hyperlipidemia and hyperglycemia, as well as for type 2 diabetes as an insulin sensitizer. 12258 - PTN1: Tyrosine-protein phosphatase non-receptor type 1 counteracts the effect of certain tyrosine kinases in protein signalling.

One of its targets is the phosphosite on the insulin receptor and several other receptor tyrosine kinases, including some from this list.

As such, it has implications for both the treatemnt of some cancers and also type 2 diabetes. 12259 - RENI: Renin is an endopeptidase that generates angiotensin 1, resulting in a blood pressure increasing signalling cascade that also causes sodium retention by the kidneys.

As such, renin inhibitors can serve to reduce blood pressure. 12260 - RXRA: Retinoid x receptor alpha is a nuclear receptor that binds retinoic acid, causing transcription of a large number of genes.

12261 - TGFR1: Transforming growth factor beta receptor 1 is a TGF-beta receptor that regulates differentiation in a number of endothelial cell types, and seems to have particular bearing on the development of reproductive tissues.

It has been targeted by studies working to develop cancer therapeutics. 12262 - THRB: Thyroid hormone receptor beta is a nuclear receptor that, when activated by thyroid hormone, initiates a large number of different genes.

Deficiencies in activity can result in thyroid hormone resistance which can cause goiter.

12263 - TRY1: Trypsin-1 is the main form of trypsinogen secreted by the pancreas.

It is an enzyme that breaks down proteins, and defective mutations of it can cause pancreatitis.

It is also a workhorse protein in modern biochemical and biophysical labs. 12264 - TRYB1: Tryptase beta-1 is a trypsin like protease that is secreted as part of Mast-cell activation.

As such it has roles in inflammation associated with asthma, and in cleaving flu's hemagglutinin surface protein (which initiates the experience of flu-like symptoms).

Attempts to produce inhibitors hve so far been difficult, but it has relevance to reducing the severity of the inflammatory response in these conditions. 12265 - VGFR2: Vascular endothelial growth factor receptor number 2 is a tyrosine kinase signaling receptor that binds the vascularization hormone, and causes tissue remodeling to form channels for blood vessel growth.

When over-active or over-expressed this protein supports the vascularization of tumor tissue, making inhibitors targeting it helpful in treating some cancers.

RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

DUD-E

Directory of Useful Decoys - Enhanced

Technical: Biotechnology
Drug Discovery / Benchmarking Data Sets

DUD-E is a widely used dataset in drug discovery research. It contains protein-ligand interactions with known binding affinities, helping researchers assess and improve the accuracy of prediction methods.


Alpha Fold

A deep learning algorithm for protein structure prediction.

Technical: Biotechnology
Drug Discovery / Protein Structure Prediction

AlphaFold is an advanced computer program that can predict the 3D structure of proteins based on their amino acid sequence. This has revolutionized drug discovery by allowing researchers to understand how proteins work and design drugs that target them more effectively.


Protein-Ligand Interactions

The binding of a protein to a small molecule ligand.

Scientific: Biotechnology
Drug Discovery / Molecular Binding

Protein-ligand interactions are essential for many biological processes, including drug action. When a drug binds to its target protein, it can alter the protein's function and produce a therapeutic effect.


Drug Discovery

The process of identifying and developing new pharmaceuticals.

Technical: Biotechnology, Pharmaceuticals
Pharmaceutical Industry / Research & Development

Drug discovery is a complex and lengthy process that involves identifying potential drug targets, screening compounds for activity, optimizing lead candidates, and conducting clinical trials to assess safety and efficacy.


Acetylcholinesterase

An enzyme that breaks down acetylcholine in the nervous system.

Technical: Biotechnology, Pharmaceuticals
Medicine / Neuropharmacology

Acetylcholinesterase is a vital enzyme that regulates nerve impulses by breaking down the neurotransmitter acetylcholine. Drugs that inhibit acetylcholinesterase are used to treat conditions like Alzheimer's disease.


Pesticides

Chemicals used to control pests.

Technical: Agriculture, Biotechnology
Agriculture / Chemical Control

Pesticides are widely used in agriculture to protect crops from insects, weeds, and diseases. However, they can also have negative impacts on human health and the environment.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:18
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 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 8,566,269 315,311 27.17 0 hrs 53 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 8,460,569 275,746 30.68 0 hrs 47 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,785,865 303,273 25.67 0 hrs 56 mins
4 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 7,649,072 272,594 28.06 0 hrs 51 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 7,076,447 76,555 92.44 0 hrs 16 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 6,410,292 285,662 22.44 1 hrs 4 mins
7 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 5,315,114 268,917 19.76 1 hrs 13 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,155,043 262,642 19.63 1 hrs 13 mins
9 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 4,782,356 258,805 18.48 1 hrs 18 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,673,941 253,052 18.47 1 hrs 18 mins
11 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 4,615,217 245,996 18.76 1 hrs 17 mins
12 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 4,339,928 222,047 19.55 1 hrs 14 mins
13 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,250,290 247,771 17.15 1 hrs 24 mins
14 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 4,055,454 177,221 22.88 1 hrs 3 mins
15 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,672,831 237,390 15.47 1 hrs 33 mins
16 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 3,376,085 231,391 14.59 1 hrs 39 mins
17 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 3,293,473 100,376 32.81 0 hrs 44 mins
18 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,291,134 127,416 25.83 0 hrs 56 mins
19 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 3,275,334 228,756 14.32 1 hrs 41 mins
20 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,234,312 227,450 14.22 1 hrs 41 mins
21 RTX A4500
GA102GL [RTX A4500]
Nvidia GA102GL 3,212,074 198,657 16.17 1 hrs 29 mins
22 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 3,150,331 212,631 14.82 1 hrs 37 mins
23 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,868,845 218,065 13.16 1 hrs 49 mins
24 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,700,843 196,792 13.72 1 hrs 45 mins
25 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 2,563,796 186,684 13.73 1 hrs 45 mins
26 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,501,889 206,526 12.11 1 hrs 59 mins
27 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,466,492 170,999 14.42 1 hrs 40 mins
28 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,433,889 192,852 12.62 1 hrs 54 mins
29 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 2,409,671 197,049 12.23 1 hrs 58 mins
30 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,391,397 157,535 15.18 1 hrs 35 mins
31 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,372,037 197,163 12.03 1 hrs 60 mins
32 GeForce RTX 2070 Mobile
TU106BM [GeForce RTX 2070 Mobile]
Nvidia TU106BM 2,284,587 202,502 11.28 2 hrs 8 mins
33 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 2,229,657 200,983 11.09 2 hrs 10 mins
34 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,055,177 163,953 12.54 1 hrs 55 mins
35 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,956,337 104,782 18.67 1 hrs 17 mins
36 GeForce RTX 2070 Mobile
TU106M [GeForce RTX 2070 Mobile]
Nvidia TU106M 1,866,923 189,930 9.83 2 hrs 26 mins
37 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,670,418 141,417 11.81 2 hrs 2 mins
38 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,634,407 181,129 9.02 2 hrs 40 mins
39 Radeon RX 7700S/7600(S)
Navi 33 [Radeon RX 7700S/7600(S)]
AMD Navi 33 1,600,785 174,241 9.19 2 hrs 37 mins
40 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,600,493 159,736 10.02 2 hrs 24 mins
41 Radeon RX 6600/6600 XT/6600M
Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 XT-XL 1,520,448 165,877 9.17 2 hrs 37 mins
42 RTX A2000 12GB
GA106 [RTX A2000 12GB]
Nvidia GA106 1,465,164 175,029 8.37 2 hrs 52 mins
43 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,454,400 26,183 55.55 0 hrs 26 mins
44 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 1,441,719 145,305 9.92 2 hrs 25 mins
45 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,380,142 113,333 12.18 1 hrs 58 mins
46 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,363,564 146,429 9.31 2 hrs 35 mins
47 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,336,295 158,946 8.41 2 hrs 51 mins
48 GeForce RTX 2070 Mobile / Max-Q Refresh
TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh]
Nvidia TU106M 1,305,046 149,036 8.76 2 hrs 44 mins
49 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 1,139,679 141,401 8.06 2 hrs 59 mins
50 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 1,058,798 140,701 7.53 3 hrs 11 mins
51 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 948,129 134,070 7.07 3 hrs 24 mins
52 GeForce GTX 1060 6GB
GP104 [GeForce GTX 1060 6GB]
Nvidia GP104 841,142 149,863 5.61 4 hrs 17 mins
53 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 807,481 124,878 6.47 3 hrs 43 mins
54 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 806,736 145,326 5.55 4 hrs 19 mins
55 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 787,112 127,412 6.18 3 hrs 53 mins
56 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 665,276 130,455 5.10 4 hrs 42 mins
57 GeForce GTX 1650 Ti Mobile
TU117M [GeForce GTX 1650 Ti Mobile]
Nvidia TU117M 526,892 118,178 4.46 5 hrs 23 mins
58 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 483,511 112,097 4.31 5 hrs 34 mins
59 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 428,786 108,912 3.94 6 hrs 6 mins
60 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 387,431 109,929 3.52 6 hrs 49 mins
61 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 321,858 105,737 3.04 7 hrs 53 mins
62 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 276,186 94,762 2.91 8 hrs 14 mins
63 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 261,107 55,896 4.67 5 hrs 8 mins
64 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 131,720 69,535 1.89 12 hrs 40 mins
65 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 93,022 36,089 2.58 9 hrs 19 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:18
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make