RESEARCH: DUD-E
FOLDING PROJECT #12209 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 120,837
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how medicines interact with proteins. They're focusing on a protein called ACES, which is important for the nervous system and is also targeted by pesticides and some drugs. By understanding how medicines bind to ACES, scientists can develop new and better 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.
.

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 - Experiments

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a benchmark dataset used in drug discovery research. It contains information about protein-ligand interactions, which are important for understanding how drugs work.


Protein-Ligand Interactions

The binding of a protein to a small molecule ligand.

Scientific: Biotechnology
Drug Discovery / Molecular Pharmacology

Protein-ligand interactions are essential for many biological processes, including drug action. A protein can bind to a small molecule (a ligand) in a specific way, leading to changes in the protein's function.


Alpha Fold

An artificial intelligence system for predicting protein structures.

Technical: Biotechnology
Drug Discovery / Protein Structure Prediction

AlphaFold is a powerful AI tool that can predict the 3D structure of proteins. This is crucial for understanding how proteins function and for designing new drugs.


Drug Discovery

The process of identifying and developing new drugs.

Industry: Pharmaceutical
Pharmaceuticals / Medicinal Chemistry

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


Pesticides

Chemicals used to kill pests.

Technical: Agribusiness
Agriculture / Pest Control

Pesticides are widely used in agriculture to protect crops from damage by insects, weeds, and other organisms. However, their use can have negative impacts on the environment and human health.


Acetylcholinesterase

An enzyme that breaks down the neurotransmitter acetylcholine.

Scientific: Biotechnology
Pharmacology / Neurotransmission

Acetylcholinesterase is a crucial enzyme in the nervous system. It helps to regulate nerve impulses by breaking down acetylcholine, a neurotransmitter involved in muscle contraction and other functions.


Neurotransmitter

A chemical messenger that transmits signals between nerve cells.

Scientific: Biotechnology
Pharmacology / Neuroscience

Neurotransmitters are essential for communication within the nervous system. They allow nerve cells to send and receive signals, which control various bodily functions, including movement, thought, and emotion.


Folding@Home

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

Technical: Biotechnology
Computational Biology / Protein Folding

Folding@Home harnesses the power of many computers to simulate how proteins fold into their 3D structures. This is important for understanding how proteins function and for designing new drugs.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:32
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 12,753,254 422,525 30.18 0 hrs 48 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 10,263,354 397,883 25.79 0 hrs 56 mins
3 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 8,280,654 369,651 22.40 1 hrs 4 mins
4 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 8,140,016 363,961 22.37 1 hrs 4 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,029,986 348,700 20.16 1 hrs 11 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,657,676 343,011 19.41 1 hrs 14 mins
7 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,471,573 341,914 18.93 1 hrs 16 mins
8 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 5,746,583 328,037 17.52 1 hrs 22 mins
9 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,948,348 312,689 15.83 1 hrs 31 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,379,047 297,473 14.72 1 hrs 38 mins
11 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 4,002,986 292,235 13.70 1 hrs 45 mins
12 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,985,327 290,882 13.70 1 hrs 45 mins
13 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,412,836 276,122 12.36 1 hrs 57 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,268,315 271,913 12.02 1 hrs 60 mins
15 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,038,827 265,768 11.43 2 hrs 6 mins
16 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,981,522 263,768 11.30 2 hrs 7 mins
17 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,957,248 264,151 11.20 2 hrs 9 mins
18 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,901,058 261,920 11.08 2 hrs 10 mins
19 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,859,209 260,282 10.99 2 hrs 11 mins
20 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,822,451 259,004 10.90 2 hrs 12 mins
21 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,467,539 243,158 10.15 2 hrs 22 mins
22 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,338,874 242,147 9.66 2 hrs 29 mins
23 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,175,936 233,122 9.33 2 hrs 34 mins
24 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 2,011,223 231,963 8.67 2 hrs 46 mins
25 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,996,034 229,016 8.72 2 hrs 45 mins
26 Quadro RTX 5000 Mobile / Max-Q
TU104GLM [Quadro RTX 5000 Mobile / Max-Q]
Nvidia TU104GLM 1,893,731 225,797 8.39 2 hrs 52 mins
27 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,821,533 222,619 8.18 2 hrs 56 mins
28 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 1,625,986 155,761 10.44 2 hrs 18 mins
29 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,435,225 206,691 6.94 3 hrs 27 mins
30 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,275,209 200,457 6.36 3 hrs 46 mins
31 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,188,756 193,533 6.14 3 hrs 54 mins
32 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,131,622 191,352 5.91 4 hrs 3 mins
33 P104-100
GP104 [P104-100]
Nvidia GP104 1,025,292 185,138 5.54 4 hrs 20 mins
34 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 668,286 160,372 4.17 5 hrs 46 mins
35 R9 Fury X/NANO
Fiji XT [R9 Fury X/NANO]
AMD Fiji XT 524,570 149,420 3.51 6 hrs 50 mins
36 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 457,275 121,001 3.78 6 hrs 21 mins
37 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 418,251 141,828 2.95 8 hrs 8 mins
38 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 403,075 135,681 2.97 8 hrs 5 mins
39 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 245,038 129,645 1.89 12 hrs 42 mins
40 Quadro K2000
GK107 [Quadro K2000]
Nvidia GK107 12,968 40,819 0.32 75 hrs 33 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:32
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make