RESEARCH: DUD-E
FOLDING PROJECT #12211 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 74,166
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how proteins interact with drugs. They're focusing on a protein called Acetylcholinesterase, which is important for the nervous system and is also a target for pesticides and some medicines. By creating accurate simulations, researchers can better understand how drugs work and develop new ones more efficiently.

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

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

DUD-E

Directory of Useful Drugs - Experimentally Determined

Technical: Pharmaceuticals
Biotechnology / Drug Discovery

DUD-E is a large dataset of protein-ligand interactions used to benchmark and evaluate computational methods for predicting how drugs bind to proteins.


Protein-Ligand Interactions

The binding of a protein to a small molecule (ligand)

Scientific: Pharmaceuticals
Biotechnology / Drug Discovery

Protein-ligand interactions are essential for many biological processes, including drug action. These interactions occur when a protein binds to a small molecule, which can be a drug, a nutrient, or another signaling molecule.


Drug Discovery

The process of identifying and developing new drugs

Technical: Pharmaceuticals
Biotechnology / Pharmaceuticals

Drug discovery is a complex and lengthy process that involves several stages, from identifying potential drug targets to testing the safety and efficacy of new compounds in humans.


Protein

A large biomolecule essential for all living organisms

Scientific: Pharmaceuticals
Biotechnology / Drug Discovery

Proteins are the building blocks of life and perform a wide range of functions in the body, including catalyzing biochemical reactions, transporting molecules, and providing structural support.


Small Molecule

A molecule with a relatively low molecular weight

Scientific: Pharmaceuticals
Biotechnology / Drug Discovery

Small molecules are often used as drugs because they can easily pass through cell membranes and interact with target proteins.


Alpha Fold

A deep learning algorithm for protein structure prediction

Technical: Artificial Intelligence
Biotechnology / Computational Biology

AlphaFold is a revolutionary AI system that can accurately predict the 3D structures of proteins, which is crucial for understanding their function and designing new drugs.


Folding@Home

A distributed computing project for protein folding simulations

Technical: Scientific Computing
Biotechnology / Computational Biology

Folding@Home harnesses the power of millions of volunteer computers to perform complex protein folding simulations, accelerating research in areas like drug discovery and disease understanding.


Acetylcholinesterase

An enzyme that breaks down acetylcholine in the nervous system

Scientific: Pharmaceuticals
Biotechnology / Neuropharmacology

Acetylcholinesterase plays a crucial role in nerve impulse transmission by breaking down acetylcholine, a neurotransmitter. Drugs that inhibit acetylcholinesterase can be used to treat Alzheimer's disease and other neurological disorders.


Nervous System

The complex network of neurons that controls bodily functions

Scientific: Pharmaceuticals
Biotechnology / Neuropharmacology

The nervous system is responsible for transmitting signals throughout the body, coordinating movement, sensory perception, thought, and emotion.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:31
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 10,826,290 222,953 48.56 0 hrs 30 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 8,321,977 202,635 41.07 0 hrs 35 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,218,057 197,105 36.62 0 hrs 39 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 6,667,641 182,560 36.52 0 hrs 39 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,065,140 181,178 33.48 0 hrs 43 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,861,449 179,345 32.68 0 hrs 44 mins
7 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,646,402 178,445 31.64 0 hrs 46 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,446,896 166,085 26.77 0 hrs 54 mins
9 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 4,330,257 162,682 26.62 0 hrs 54 mins
10 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,012,441 161,085 24.91 0 hrs 58 mins
11 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,869,577 157,190 24.62 0 hrs 58 mins
12 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,608,858 155,799 23.16 1 hrs 2 mins
13 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 3,573,323 155,690 22.95 1 hrs 3 mins
14 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,190,710 149,076 21.40 1 hrs 7 mins
15 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,174,427 148,965 21.31 1 hrs 8 mins
16 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 3,115,018 142,838 21.81 1 hrs 6 mins
17 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,111,541 148,940 20.89 1 hrs 9 mins
18 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,953,794 145,280 20.33 1 hrs 11 mins
19 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,867,989 142,879 20.07 1 hrs 12 mins
20 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,710,713 140,707 19.26 1 hrs 15 mins
21 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,588,900 139,236 18.59 1 hrs 17 mins
22 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,561,438 134,284 19.07 1 hrs 15 mins
23 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,430,100 135,393 17.95 1 hrs 20 mins
24 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,381,153 135,066 17.63 1 hrs 22 mins
25 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,312,343 133,798 17.28 1 hrs 23 mins
26 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,103,723 130,110 16.17 1 hrs 29 mins
27 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,951,768 122,261 15.96 1 hrs 30 mins
28 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,919,800 126,290 15.20 1 hrs 35 mins
29 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,908,433 127,036 15.02 1 hrs 36 mins
30 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,655,873 116,548 14.21 1 hrs 41 mins
31 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,254,414 109,347 11.47 2 hrs 6 mins
32 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,243,385 108,141 11.50 2 hrs 5 mins
33 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,197,285 107,691 11.12 2 hrs 10 mins
34 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,168,868 107,337 10.89 2 hrs 12 mins
35 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,121,133 105,353 10.64 2 hrs 15 mins
36 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 863,776 96,114 8.99 2 hrs 40 mins
37 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 833,393 95,337 8.74 2 hrs 45 mins
38 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 817,034 94,785 8.62 2 hrs 47 mins
39 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 570,151 84,296 6.76 3 hrs 33 mins
40 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 535,597 82,378 6.50 3 hrs 41 mins
41 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 227,135 61,924 3.67 6 hrs 33 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:31
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make