RESEARCH: DUD-E
FOLDING PROJECT #12218 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 144,173
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project simulates how proteins interact with small molecules (like drugs) using powerful computer models. The goal is to understand these interactions better, which can speed up drug discovery and make it more efficient. Scientists use a well-known dataset called DUD-E, which contains information about many different proteins and their interactions with various molecules.

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

Dataset of protein-ligand interactions for benchmarking.

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a widely used dataset in the field of drug discovery. It contains information about proteins and small molecules that bind to them, which helps researchers develop and test new drugs.


Alpha Fold

An AI system for predicting protein structures.

Scientific: Pharmaceuticals
Biotechnology / Protein Structure Prediction

AlphaFold is a groundbreaking artificial intelligence program that can predict the 3D structure of proteins. This has enormous implications for drug discovery, as it allows researchers to understand how proteins interact with each other and with potential drugs.


Protein-Ligand Interactions

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

Scientific: Biotechnology
Pharmacology / Drug Discovery

Protein-ligand interactions are essential for many biological processes, including drug action. When a drug binds to its target protein, it can alter the protein's function and produce a therapeutic effect.


Drug Discovery

The process of identifying and developing new medications.

Technical: Biotechnology
Pharmaceuticals / Research & Development

Drug discovery is a complex and lengthy process that involves several stages, from identifying potential drug targets to testing and manufacturing the final product. It is a crucial part of the pharmaceutical industry.


Acetylcholinesterase

An enzyme that breaks down acetylcholine.

Scientific: Biotechnology
Pharmacology / Neurology

Acetylcholinesterase is a crucial enzyme in the nervous system. It plays a role in nerve signal transmission by breaking down acetylcholine, a neurotransmitter. Many drugs target acetylcholinesterase to treat conditions like Alzheimer's disease.


Pesticides

Chemicals used to kill insects and other pests.

Technical: Chemicals
Agriculture / Pest Control

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

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:27
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 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 9,284,074 479,926 19.34 1 hrs 14 mins
2 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 9,118,433 478,052 19.07 1 hrs 15 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,876,635 451,931 17.43 1 hrs 23 mins
4 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,017,139 434,915 16.13 1 hrs 29 mins
5 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,882,512 432,036 15.93 1 hrs 30 mins
6 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 6,730,868 426,362 15.79 1 hrs 31 mins
7 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,984,317 387,401 12.87 1 hrs 52 mins
8 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,704,980 378,977 12.41 1 hrs 56 mins
9 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,641,288 390,661 11.88 2 hrs 1 mins
10 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,404,805 375,248 11.74 2 hrs 3 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,710,494 355,082 10.45 2 hrs 18 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,487,059 347,039 10.05 2 hrs 23 mins
13 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,375,931 341,504 9.89 2 hrs 26 mins
14 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,283,139 341,665 9.61 2 hrs 30 mins
15 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,116,512 333,555 9.34 2 hrs 34 mins
16 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,916,887 327,654 8.90 2 hrs 42 mins
17 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,892,526 322,639 8.97 2 hrs 41 mins
18 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,709,069 314,388 8.62 2 hrs 47 mins
19 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,632,075 315,579 8.34 2 hrs 53 mins
20 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,466,016 309,155 7.98 3 hrs 1 mins
21 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,354,218 306,022 7.69 3 hrs 7 mins
22 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,222,883 299,321 7.43 3 hrs 14 mins
23 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,940,128 246,847 7.86 3 hrs 3 mins
24 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,927,050 276,864 6.96 3 hrs 27 mins
25 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,298,365 249,681 5.20 4 hrs 37 mins
26 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,276,772 254,282 5.02 4 hrs 47 mins
27 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,186,059 247,488 4.79 5 hrs 0 mins
28 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,071,753 239,525 4.47 5 hrs 22 mins
29 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 755,008 209,877 3.60 6 hrs 40 mins
30 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 707,322 219,082 3.23 7 hrs 26 mins
31 GeForce GTX 1060 Mobile
GP106M [GeForce GTX 1060 Mobile]
Nvidia GP106M 585,448 191,633 3.06 7 hrs 51 mins
32 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 538,914 162,407 3.32 7 hrs 14 mins
33 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 287,590 173,010 1.66 14 hrs 26 mins
34 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 251,656 146,411 1.72 13 hrs 58 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:27
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make