RESEARCH: DUD-E
FOLDING PROJECT #12204 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 81,085
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 found in a special dataset called DUD-E, which includes many important proteins for medical research. By simulating these interactions accurately, scientists 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.

proteins

Large biomolecules essential for various bodily functions.

scientific: biotechnology
medicine / pharmacology

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


protein-ligand interactions

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

scientific: biotechnology
medicine / pharmacology

Protein-ligand interactions are crucial for many biological processes, including drug action and enzyme function. When a small molecule binds to a protein, it can alter the protein's shape or activity.


drug discovery

The process of identifying and developing new medications.

technical: biotechnology
medicine / pharmacology

Drug discovery is a complex and lengthy process that involves identifying promising drug candidates, testing their safety and efficacy in preclinical studies, and ultimately bringing them to market.


DUD-E

Directory of Useful Drugs - Experimental.

acronym: academia
medicine / biotechnology

DUD-E is a widely used benchmark dataset for evaluating protein-ligand interaction prediction methods. It contains diverse proteins and a large collection of small molecules with known binding affinities.


Alpha Fold

An AI system for predicting protein structures.

acronym: academia
medicine / biotechnology

AlphaFold is a groundbreaking AI system developed by DeepMind that can accurately predict the 3D structures of proteins. This has revolutionized protein research and has numerous applications in drug discovery and disease understanding.


Folding@Home

A distributed computing project for protein folding simulations.

acronym: academia
medicine / biotechnology

Folding@Home utilizes the processing power of volunteer computers to perform complex simulations of protein folding. These simulations contribute to our understanding of protein structure and function.


acetylcholinesterase

An enzyme that breaks down acetylcholine in the nervous system.

scientific: pharmacology
medicine / neurology

Acetylcholinesterase is a crucial enzyme for regulating nerve impulses. It breaks down acetylcholine, a neurotransmitter involved in muscle movement, memory, and learning.


pesticide

A substance used to kill pests.

technical: chemicals
agriculture / entomology

Pesticides are chemicals designed to control unwanted organisms, such as insects, weeds, and fungi. While they can be beneficial for agriculture, overuse can have negative environmental impacts.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:35
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 11,837,909 253,007 46.79 0 hrs 31 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 8,908,359 230,353 38.67 0 hrs 37 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 8,152,602 224,835 36.26 0 hrs 40 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,297,275 216,501 33.71 0 hrs 43 mins
5 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,347,568 207,402 30.61 0 hrs 47 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,479,923 185,002 24.22 0 hrs 59 mins
7 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 4,347,966 182,242 23.86 1 hrs 0 mins
8 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,000,108 177,883 22.49 1 hrs 4 mins
9 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,637,582 172,576 21.08 1 hrs 8 mins
10 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,323,066 166,886 19.91 1 hrs 12 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,176,115 164,358 19.32 1 hrs 15 mins
12 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 3,023,827 158,096 19.13 1 hrs 15 mins
13 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,942,460 160,693 18.31 1 hrs 19 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,900,315 159,047 18.24 1 hrs 19 mins
15 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,655,244 155,170 17.11 1 hrs 24 mins
16 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,303,440 147,708 15.59 1 hrs 32 mins
17 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,240,778 138,174 16.22 1 hrs 29 mins
18 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,995,083 140,986 14.15 1 hrs 42 mins
19 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,852,006 136,825 13.54 1 hrs 46 mins
20 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,822,461 136,612 13.34 1 hrs 48 mins
21 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,767,034 136,363 12.96 1 hrs 51 mins
22 Quadro RTX 5000 Mobile / Max-Q
TU104GLM [Quadro RTX 5000 Mobile / Max-Q]
Nvidia TU104GLM 1,712,513 133,943 12.79 1 hrs 53 mins
23 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,593,191 131,020 12.16 1 hrs 58 mins
24 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,191,179 118,667 10.04 2 hrs 23 mins
25 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,145,539 116,732 9.81 2 hrs 27 mins
26 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,002,433 100,159 10.01 2 hrs 24 mins
27 P104-100
GP104 [P104-100]
Nvidia GP104 992,687 111,549 8.90 2 hrs 42 mins
28 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 539,137 90,628 5.95 4 hrs 2 mins
29 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 501,320 88,686 5.65 4 hrs 15 mins
30 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 398,285 82,428 4.83 4 hrs 58 mins
31 R9 Fury X/NANO
Fiji XT [R9 Fury X/NANO]
AMD Fiji XT 367,823 84,090 4.37 5 hrs 29 mins
32 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 301,498 68,747 4.39 5 hrs 28 mins
33 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 225,299 68,168 3.31 7 hrs 16 mins
34 Ryzen 7000 Series iGPU
Raphael [Ryzen 7000 Series iGPU]
AMD Raphael 14,002 24,256 0.58 41 hrs 35 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

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