RESEARCH: DUD-E
FOLDING PROJECT #12207 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 82,887
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 proteins from the DUD-E dataset, which is a collection of well-studied proteins and their interactions with different compounds. By simulating these interactions, researchers can better understand how drugs work and develop new, more effective 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 widely used dataset in drug discovery research. It contains diverse proteins bound to various small molecules, allowing researchers to benchmark and improve methods for predicting protein-ligand interactions.


protein-ligand interactions

The binding of a protein and a ligand (e.g., drug molecule) to each other.

Scientific: Biotechnology, Pharmaceuticals
Drug Discovery / Pharmacology

Protein-ligand interactions are essential for many biological processes. In drug discovery, researchers study these interactions to understand how drugs bind to target proteins and exert their effects.


drug discovery

The process of identifying and developing new drugs.

Technical: Biotechnology, Pharmaceuticals
Pharmaceuticals / Research & Development

Drug discovery is a complex process that involves multiple stages, from identifying potential drug targets to testing and manufacturing new medications. It aims to develop safe and effective treatments for diseases.


Alpha Fold

A deep learning algorithm that predicts the 3D structure of proteins.

Technical: Biotechnology, Artificial Intelligence
Biotechnology / Protein Structure Prediction

AlphaFold is a revolutionary AI system that accurately predicts protein structures from amino acid sequences. This breakthrough has immense implications for understanding biological processes and developing new drugs.


Folding@Home

A distributed computing project that uses volunteers' computers to simulate protein folding.

Technical: Biotechnology, Research
Biotechnology / Distributed Computing

Folding@Home harnesses the power of many computers to perform complex simulations of protein folding. This collaborative effort accelerates research in various fields, including drug discovery and disease understanding.


Acetylcholinesterase

An enzyme that breaks down acetylcholine, a neurotransmitter.

Scientific: Biotechnology, Pharmaceuticals
Pharmacology / Neurotransmission

Acetylcholinesterase is crucial for regulating nerve impulses. It controls the breakdown of acetylcholine, ensuring proper communication between nerve cells. Medications targeting this enzyme are used to treat conditions like Alzheimer's disease.


Torpedo Californica

The scientific name for the California electric ray.

Scientific: Research
Biology / Marine Biology

Torpedo Californica, commonly known as the California electric ray, is a marine species known for its ability to generate electrical shocks. It has been studied extensively for its neurotransmitter receptors, contributing to advancements in biomedical research.


Neurotransmitter

A chemical messenger that transmits signals between nerve cells.

Scientific: Biotechnology, Pharmaceuticals
Neuroscience / Cellular Communication

Neurotransmitters are crucial for communication within the nervous system. They allow nerve cells to transmit signals, controlling various bodily functions, emotions, and behaviors.

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 11,626,396 252,233 46.09 0 hrs 31 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 9,638,059 238,026 40.49 0 hrs 36 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,483,909 220,133 34.00 0 hrs 42 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,981,866 213,917 32.64 0 hrs 44 mins
5 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,166,179 206,368 29.88 0 hrs 48 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,565,003 187,346 24.37 0 hrs 59 mins
7 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 3,838,525 176,164 21.79 1 hrs 6 mins
8 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,645,124 172,841 21.09 1 hrs 8 mins
9 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,636,581 172,130 21.13 1 hrs 8 mins
10 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,193,205 166,531 19.17 1 hrs 15 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,107,393 163,990 18.95 1 hrs 16 mins
12 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,960,130 160,864 18.40 1 hrs 18 mins
13 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,800,753 158,377 17.68 1 hrs 21 mins
14 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,604,500 156,149 16.68 1 hrs 26 mins
15 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,555,443 154,118 16.58 1 hrs 27 mins
16 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,531,668 149,724 16.91 1 hrs 25 mins
17 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,281,624 146,968 15.52 1 hrs 33 mins
18 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,053,655 143,852 14.28 1 hrs 41 mins
19 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,006,932 142,358 14.10 1 hrs 42 mins
20 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,960,915 141,368 13.87 1 hrs 44 mins
21 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,745,137 135,650 12.86 1 hrs 52 mins
22 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,622,064 132,347 12.26 1 hrs 57 mins
23 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,339,896 123,789 10.82 2 hrs 13 mins
24 P102-100
GP102 [P102-100]
Nvidia GP102 1,324,500 122,912 10.78 2 hrs 14 mins
25 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,276,334 122,522 10.42 2 hrs 18 mins
26 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,193,483 119,509 9.99 2 hrs 24 mins
27 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,182,552 119,214 9.92 2 hrs 25 mins
28 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,113,929 117,016 9.52 2 hrs 31 mins
29 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,087,169 115,151 9.44 2 hrs 33 mins
30 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 809,742 104,983 7.71 3 hrs 7 mins
31 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 805,573 105,679 7.62 3 hrs 9 mins
32 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 792,526 104,502 7.58 3 hrs 10 mins
33 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 738,073 94,832 7.78 3 hrs 5 mins
34 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 676,005 78,319 8.63 2 hrs 47 mins
35 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 602,173 96,107 6.27 3 hrs 50 mins
36 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 468,605 84,628 5.54 4 hrs 20 mins
37 R9 Fury X/NANO
Fiji XT [R9 Fury X/NANO]
AMD Fiji XT 410,575 84,097 4.88 4 hrs 55 mins
38 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 385,772 82,180 4.69 5 hrs 7 mins
39 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 260,452 77,114 3.38 7 hrs 6 mins
40 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 221,787 68,221 3.25 7 hrs 23 mins
41 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 209,021 65,255 3.20 7 hrs 30 mins
42 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 200,048 65,066 3.07 7 hrs 48 mins
43 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 147,603 59,577 2.48 9 hrs 41 mins
44 R7 370/R9 270X/370X
Curacao XT/Trinidad XT [R7 370/R9 270X/370X]
AMD Curacao XT/Trinidad XT 76,036 47,982 1.58 15 hrs 9 mins
45 Quadro P600
GP107GL [Quadro P600]
Nvidia GP107GL 74,180 54,710 1.36 17 hrs 42 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