RESEARCH: DUD-E
FOLDING PROJECT #12245 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 49,100Core: 0x23
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 protein called acetylcholinesterase which is important for nerve function and is a target for both pesticides and medicines. By creating accurate simulations, they hope to improve methods for designing new drugs.
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
DUD-E
Dataset of diverse proteins and ligands for benchmarking.
DUD-E stands for Directory of Useful Decoys - Extended. It's a widely used dataset in the field of drug discovery to test and compare different methods for predicting how molecules bind to proteins.
Protein-Ligand Interactions
The binding of a protein to a ligand (small molecule).
Protein-ligand interactions are essential for many biological processes. In drug discovery, understanding how drugs bind to proteins is crucial for designing effective treatments.
Drug Discovery
The process of identifying and developing new drugs.
Drug discovery is a complex and lengthy process that involves identifying promising drug candidates, testing their safety and efficacy, and ultimately bringing them to market.
AlphaFold
An AI system for predicting protein structures.
AlphaFold is a groundbreaking artificial intelligence system developed by DeepMind that can accurately predict the 3D structures of proteins. This has revolutionized our understanding of protein function and has immense potential for drug discovery.
Folding@Home
A distributed computing project that uses volunteers' computers to simulate protein folding.
Folding@Home is a citizen science project that harnesses the power of millions of computers worldwide to simulate the complex process of protein folding. This helps researchers understand how proteins fold and function, which is crucial for drug discovery and other biomedical applications.
Acetylcholinesterase
An enzyme that breaks down acetylcholine.
Acetylcholinesterase is a vital enzyme in the nervous system that helps regulate nerve impulses. It breaks down acetylcholine, a neurotransmitter, after it has transmitted a signal across a synapse. This ensures proper communication between nerve cells.
Pesticides
Chemicals used to control pests.
Pesticides are chemicals designed to kill or repel unwanted organisms that can damage crops or spread disease. While they play a crucial role in agriculture, their use can have negative environmental and health impacts.
Neurotransmitter
A chemical that transmits signals between nerve cells.
Neurotransmitters are essential for communication within the nervous system. They carry signals across synapses, the gaps between nerve cells, allowing for coordinated activity and responses.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:12|
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 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,492,192 | 125,000 | 51.94 | 0 hrs 28 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,269,447 | 119,897 | 52.29 | 0 hrs 28 mins |
| 3 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 6,065,716 | 133,592 | 45.40 | 0 hrs 32 mins |
| 4 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,806,511 | 131,347 | 44.21 | 0 hrs 33 mins |
| 5 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,986,625 | 126,186 | 39.52 | 0 hrs 36 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,672,120 | 19,573 | 238.70 | 0 hrs 6 mins |
| 7 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,585,323 | 121,003 | 37.89 | 0 hrs 38 mins |
| 8 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,481,227 | 121,939 | 36.75 | 0 hrs 39 mins |
| 9 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 3,653,206 | 109,153 | 33.47 | 0 hrs 43 mins |
| 10 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,526,044 | 112,819 | 31.25 | 0 hrs 46 mins |
| 11 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,380,923 | 107,956 | 31.32 | 0 hrs 46 mins |
| 12 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 3,374,547 | 88,815 | 38.00 | 0 hrs 38 mins |
| 13 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,269,619 | 108,828 | 30.04 | 0 hrs 48 mins |
| 14 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,152,591 | 106,831 | 29.51 | 0 hrs 49 mins |
| 15 | RTX A4500 GA102GL [RTX A4500] |
Nvidia | GA102GL | 3,115,074 | 107,833 | 28.89 | 0 hrs 50 mins |
| 16 | TITAN RTX TU102 [TITAN RTX] 16310 |
Nvidia | TU102 | 3,109,026 | 107,037 | 29.05 | 0 hrs 50 mins |
| 17 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,108,730 | 52,868 | 58.80 | 0 hrs 24 mins |
| 18 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,827,920 | 100,073 | 28.26 | 0 hrs 51 mins |
| 19 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,788,270 | 103,034 | 27.06 | 0 hrs 53 mins |
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| 20 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,752,664 | 102,762 | 26.79 | 0 hrs 54 mins |
| 21 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,687,849 | 57,463 | 46.78 | 0 hrs 31 mins |
| 22 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,368,585 | 98,090 | 24.15 | 0 hrs 60 mins |
| 23 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,365,827 | 95,494 | 24.77 | 0 hrs 58 mins |
| 24 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,362,500 | 98,952 | 23.88 | 1 hrs 0 mins |
| 25 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,301,731 | 93,133 | 24.71 | 0 hrs 58 mins |
| 26 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,294,609 | 92,131 | 24.91 | 0 hrs 58 mins |
| 27 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 2,140,794 | 47,800 | 44.79 | 0 hrs 32 mins |
| 28 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,108,286 | 93,389 | 22.58 | 1 hrs 4 mins |
| 29 | GeForce RTX 2070 Mobile TU106BM [GeForce RTX 2070 Mobile] |
Nvidia | TU106BM | 2,092,673 | 94,001 | 22.26 | 1 hrs 5 mins |
| 30 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,065,639 | 79,028 | 26.14 | 0 hrs 55 mins |
| 31 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,040,455 | 67,931 | 30.04 | 0 hrs 48 mins |
| 32 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,943,948 | 91,691 | 21.20 | 1 hrs 8 mins |
| 33 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,883,376 | 86,685 | 21.73 | 1 hrs 6 mins |
| 34 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,531,096 | 78,942 | 19.40 | 1 hrs 14 mins |
| 35 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,511,405 | 84,292 | 17.93 | 1 hrs 20 mins |
| 36 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 1,505,312 | 97,546 | 15.43 | 1 hrs 33 mins |
| 37 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,462,358 | 36,452 | 40.12 | 0 hrs 36 mins |
| 38 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,403,605 | 81,407 | 17.24 | 1 hrs 24 mins |
| 39 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,389,787 | 55,599 | 25.00 | 0 hrs 58 mins |
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| 40 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,375,348 | 86,582 | 15.88 | 1 hrs 31 mins |
| 41 | Radeon PRO W6600 Navi 23 XL [Radeon PRO W6600] |
AMD | Navi 23 XL | 1,344,060 | 81,315 | 16.53 | 1 hrs 27 mins |
| 42 | GeForce RTX 2070 Mobile TU106M [GeForce RTX 2070 Mobile] |
Nvidia | TU106M | 1,320,602 | 76,695 | 17.22 | 1 hrs 24 mins |
| 43 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,296,000 | 8,670 | 149.48 | 0 hrs 10 mins |
| 44 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 1,256,279 | 70,078 | 17.93 | 1 hrs 20 mins |
| 45 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,214,692 | 87,443 | 13.89 | 1 hrs 44 mins |
| 46 | RTX A2000 12GB GA106 [RTX A2000 12GB] |
Nvidia | GA106 | 1,195,330 | 77,638 | 15.40 | 1 hrs 34 mins |
| 47 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,168,547 | 68,835 | 16.98 | 1 hrs 25 mins |
| 48 | Radeon RX 7700S/7600(S) Navi 33 [Radeon RX 7700S/7600(S)] |
AMD | Navi 33 | 1,062,002 | 66,024 | 16.09 | 1 hrs 30 mins |
| 49 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 963,631 | 74,605 | 12.92 | 1 hrs 51 mins |
| 50 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 948,323 | 60,551 | 15.66 | 1 hrs 32 mins |
| 51 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 834,545 | 22,236 | 37.53 | 0 hrs 38 mins |
| 52 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 786,266 | 73,167 | 10.75 | 2 hrs 14 mins |
| 53 | Radeon RX 6600/6600 XT/6600M Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 XT-XL | 767,858 | 53,091 | 14.46 | 1 hrs 40 mins |
| 54 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 690,692 | 74,039 | 9.33 | 2 hrs 34 mins |
| 55 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 604,800 | 12,920 | 46.81 | 0 hrs 31 mins |
| 56 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 499,762 | 56,428 | 8.86 | 2 hrs 43 mins |
| 57 | GeForce GTX 1650 Ti Mobile TU117M [GeForce GTX 1650 Ti Mobile] |
Nvidia | TU117M | 447,209 | 56,366 | 7.93 | 3 hrs 1 mins |
| 58 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 416,331 | 53,825 | 7.73 | 3 hrs 6 mins |
| 59 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 399,871 | 48,171 | 8.30 | 2 hrs 53 mins |
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| 60 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 396,993 | 50,433 | 7.87 | 3 hrs 3 mins |
| 61 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 280,015 | 49,589 | 5.65 | 4 hrs 15 mins |
| 62 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 252,647 | 12,348 | 20.46 | 1 hrs 10 mins |
| 63 | GeForce MX150 GP107M [GeForce MX150] |
Nvidia | GP107M | 168,280 | 46,906 | 3.59 | 6 hrs 41 mins |
| 64 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 138,876 | 33,260 | 4.18 | 5 hrs 45 mins |
| 65 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 124,521 | 10,404 | 11.97 | 2 hrs 0 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:12|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|