RESEARCH: DUD-E
FOLDING PROJECT #12206 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 82,772
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 targeted by both pesticides and medications. By simulating these interactions, researchers can better understand how drugs work and develop new ones.

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: Pharmaceutical
Biotechnology / Drug Discovery

DUD-E is a benchmark dataset used in computational drug discovery. It contains protein-ligand pairs with known binding affinities, providing a standardized platform for evaluating the performance of prediction algorithms.


Protein

Large biomolecules essential for various biological functions.

Scientific: Pharmaceutical
Biotechnology / Structural Biology

Proteins are complex molecules found in all living organisms. They perform a wide range of functions, including catalyzing reactions, transporting molecules, and providing structural support.


Ligand

Molecules that bind to a specific receptor or protein.

Scientific: Pharmaceutical
Biotechnology / Pharmacology

Ligands are molecules that interact with biological targets, such as proteins or receptors. This binding can trigger various cellular responses.


Drug Discovery

The process of identifying and developing new medications.

Technical: Pharmaceutical
Biotechnology / Pharmaceutical Research

Drug discovery is a complex multi-stage process involving the identification of potential drug targets, screening for compounds with desired activity, and development of safe and effective medications.


Alpha Fold

A deep learning algorithm for protein structure prediction.

Technical: Pharmaceutical
Biotechnology / Structural Biology

AlphaFold is a groundbreaking artificial intelligence system that can accurately predict the three-dimensional structures of proteins. This has significant implications for drug discovery and understanding biological processes.


Folding@Home

Distributed computing project for protein folding simulations.

Technical: Research
Biotechnology / Computational Biology

Folding@Home utilizes the processing power of volunteer computers to simulate protein folding, contributing to research in areas such as drug design and disease understanding.


Acetylcholinesterase

Enzyme that breaks down acetylcholine in the nervous system.

Scientific: Pharmaceutical
Biotechnology / Neurobiology

Acetylcholinesterase is a crucial enzyme that regulates neurotransmission by breaking down acetylcholine, a neurotransmitter involved in muscle movement, memory, and learning.


Pesticides

Chemicals used to control pests.

Technical: Agricultural
Agriculture / Pest Control

Pesticides are substances designed to kill or repel unwanted organisms, such as insects, rodents, and weeds. Their use is widespread in agriculture to protect crops and improve yields.


Drugs

Substances used to treat or prevent diseases.

Technical: Pharmaceutical
Medicine / Pharmacology

Drugs are chemical substances that have a specific effect on the body. They are used to diagnose, treat, cure, or prevent diseases.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:33
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 11,109,828 251,787 44.12 0 hrs 33 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 8,666,561 238,419 36.35 0 hrs 40 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,908,459 227,674 34.74 0 hrs 41 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,959,094 216,290 32.17 0 hrs 45 mins
5 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 6,224,517 206,270 30.18 0 hrs 48 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,155,954 209,789 29.34 0 hrs 49 mins
7 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,572,938 189,478 24.13 0 hrs 60 mins
8 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,017,677 181,654 22.12 1 hrs 5 mins
9 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,356,863 171,271 19.60 1 hrs 13 mins
10 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,157,869 167,870 18.81 1 hrs 17 mins
11 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,084,783 166,058 18.58 1 hrs 18 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,948,220 163,866 17.99 1 hrs 20 mins
13 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,914,597 162,632 17.92 1 hrs 20 mins
14 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 2,877,052 179,255 16.05 1 hrs 30 mins
15 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,837,224 162,265 17.49 1 hrs 22 mins
16 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,743,592 159,515 17.20 1 hrs 24 mins
17 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 2,660,781 133,321 19.96 1 hrs 12 mins
18 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,582,053 156,051 16.55 1 hrs 27 mins
19 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,467,075 153,444 16.08 1 hrs 30 mins
20 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,418,707 153,809 15.73 1 hrs 32 mins
21 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,073,515 144,711 14.33 1 hrs 40 mins
22 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,996,279 143,937 13.87 1 hrs 44 mins
23 Quadro RTX 5000 Mobile / Max-Q
TU104GLM [Quadro RTX 5000 Mobile / Max-Q]
Nvidia TU104GLM 1,786,343 138,884 12.86 1 hrs 52 mins
24 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,579,879 132,976 11.88 2 hrs 1 mins
25 P102-100
GP102 [P102-100]
Nvidia GP102 1,423,512 112,549 12.65 1 hrs 54 mins
26 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,312,050 125,347 10.47 2 hrs 18 mins
27 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,164,766 120,126 9.70 2 hrs 29 mins
28 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,104,993 117,992 9.36 2 hrs 34 mins
29 P104-100
GP104 [P104-100]
Nvidia GP104 1,053,785 116,435 9.05 2 hrs 39 mins
30 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 992,068 112,848 8.79 2 hrs 44 mins
31 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 846,147 108,983 7.76 3 hrs 5 mins
32 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 803,601 106,314 7.56 3 hrs 11 mins
33 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 645,621 99,174 6.51 3 hrs 41 mins
34 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 600,553 95,017 6.32 3 hrs 48 mins
35 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 569,725 95,082 5.99 4 hrs 0 mins
36 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 464,212 88,349 5.25 4 hrs 34 mins
37 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 420,762 86,083 4.89 4 hrs 55 mins
38 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 397,406 81,081 4.90 4 hrs 54 mins
39 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 380,427 82,920 4.59 5 hrs 14 mins
40 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 228,375 69,946 3.27 7 hrs 21 mins
41 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 226,344 69,077 3.28 7 hrs 19 mins
42 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 146,362 61,748 2.37 10 hrs 8 mins
43 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 115,769 55,790 2.08 11 hrs 34 mins
44 R7 370/R9 270X/370X
Curacao XT/Trinidad XT [R7 370/R9 270X/370X]
AMD Curacao XT/Trinidad XT 85,819 47,293 1.81 13 hrs 14 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:33
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make