RESEARCH: DUD-E
FOLDING PROJECT #12205 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 77,423
Core: 0x22
Status: Public

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 set of proteins called DUD-E, which are important for health and have been studied a lot. By simulating these interactions accurately, 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. 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 Decoys - Enhanced

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a benchmark dataset used to evaluate the accuracy of computational methods for predicting protein-ligand interactions. It contains diverse proteins with experimentally determined binding affinities for numerous small molecules.


Protein-Ligand Interactions

The binding of a protein to a small molecule ligand, often leading to biological effects.

Scientific: Biotechnology, Pharmaceuticals
Pharmacology / Drug Discovery

Protein-ligand interactions are essential for many biological processes. In drug discovery, understanding how drugs bind to target proteins is crucial for developing new therapies.


Drug Discovery

The process of identifying and developing new drugs.

Technical: Biotechnology, Pharmaceuticals
Pharmaceuticals / Biotechnology

Drug discovery is a complex and multi-stage process that involves identifying potential drug candidates, testing their efficacy and safety, and ultimately bringing them to market.


Alpha Fold

A deep learning algorithm for predicting protein structures.

Scientific: Biotechnology
Biotechnology / Protein Structure Prediction

AlphaFold is a revolutionary AI system that can predict the 3D structure of proteins with remarkable accuracy. This has immense implications for understanding protein function and designing new drugs.


Folding@Home

A distributed computing project for simulating protein folding.

Technical: Biotechnology
Biotechnology / Computational Biology

Folding@Home harnesses the power of volunteer computer resources to simulate protein folding and other complex biological processes.


Acetylcholinesterase

An enzyme that breaks down acetylcholine in the nervous system.

Scientific: Biotechnology, Pharmaceuticals
Pharmacology / Neurology

Acetylcholinesterase plays a crucial role in nerve impulse transmission by regulating the levels of acetylcholine, a neurotransmitter.


Pesticides

Chemicals used to control pests.

Technical: Agriculture, Chemicals
Agriculture / Environmental Science

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

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:34
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 9,896,770 225,584 43.87 0 hrs 33 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 8,540,984 210,586 40.56 0 hrs 36 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,245,918 207,354 34.94 0 hrs 41 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,573,998 197,114 33.35 0 hrs 43 mins
5 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 5,507,438 192,129 28.67 0 hrs 50 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,964,295 181,689 27.32 0 hrs 53 mins
7 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,288,593 167,850 25.55 0 hrs 56 mins
8 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,156,397 171,707 24.21 0 hrs 59 mins
9 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,699,949 166,336 22.24 1 hrs 5 mins
10 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 3,107,914 156,946 19.80 1 hrs 13 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,101,483 155,874 19.90 1 hrs 12 mins
12 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,026,703 154,198 19.63 1 hrs 13 mins
13 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,024,317 152,712 19.80 1 hrs 13 mins
14 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,685,805 146,341 18.35 1 hrs 18 mins
15 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,368,477 142,621 16.61 1 hrs 27 mins
16 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,298,456 137,676 16.69 1 hrs 26 mins
17 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,036,112 132,705 15.34 1 hrs 34 mins
18 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,023,934 134,903 15.00 1 hrs 36 mins
19 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,976,269 133,802 14.77 1 hrs 37 mins
20 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,833,398 131,311 13.96 1 hrs 43 mins
21 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,269,782 115,607 10.98 2 hrs 11 mins
22 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,209,436 113,615 10.65 2 hrs 15 mins
23 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,206,865 113,881 10.60 2 hrs 16 mins
24 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,159,474 101,651 11.41 2 hrs 6 mins
25 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,004,870 101,808 9.87 2 hrs 26 mins
26 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 990,302 105,736 9.37 2 hrs 34 mins
27 P104-100
GP104 [P104-100]
Nvidia GP104 980,748 106,262 9.23 2 hrs 36 mins
28 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 862,137 101,480 8.50 2 hrs 49 mins
29 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 833,465 100,467 8.30 2 hrs 54 mins
30 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 743,884 85,909 8.66 2 hrs 46 mins
31 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 603,914 90,376 6.68 3 hrs 35 mins
32 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 563,537 88,606 6.36 3 hrs 46 mins
33 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 506,457 86,718 5.84 4 hrs 7 mins
34 Ryzen 7000 Series iGPU
Raphael [Ryzen 7000 Series iGPU]
AMD Raphael 438,449 32,476 13.50 1 hrs 47 mins
35 R9 Fury X/NANO
Fiji XT [R9 Fury X/NANO]
AMD Fiji XT 404,865 78,131 5.18 4 hrs 38 mins
36 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 397,542 78,498 5.06 4 hrs 44 mins
37 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 355,220 75,982 4.68 5 hrs 8 mins
38 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 228,600 66,482 3.44 6 hrs 59 mins
39 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 114,667 51,967 2.21 10 hrs 53 mins
40 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 112,362 57,368 1.96 12 hrs 15 mins
41 Quadro K2200
GM107GL [Quadro K2200]
Nvidia GM107GL 73,782 30,688 2.40 9 hrs 59 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:34
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make