RESEARCH: CANCER
FOLDING PROJECT #18011 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 73,445Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project investigates new cancer drugs called PROTACs that break down cancer-causing proteins instead of just blocking them. This approach is more effective against drug-resistant cancers and can target parts of proteins that regular drugs can't. Scientists use computer models to design these drugs and make the data publicly available.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
This project investigates anti-cancer drugs that might overcome drug resistance.
The targets considered are major oncogenes like SMARCA2, BRD4, Bcl and BTK.
Drug-resistance is a major and unavoidable problem and presently only 20–25NULLof all protein targets are studied.
Moreover, the focus of current explorations of targets are their enzymatic functions, while ignoring the functions from their scaffold moiety.
Roivant's drug discovery choose to focus on a promising new technology, PROteolysis TArgeting Chimeras (PROTACs) which regulates protein function by degrading target proteins instead of inhibiting them.
This method provided more sensitivity to drug-resistant targets, better selectivity, and a greater chance to affect the nonenzymatic functions of targeted proteins.
Roivant is leading in the general paradigm shift that looks at the kinetics of reactions instead of binding thermodynamics for its PROTACs drug discovery.
Specifically, by understanding the balance between changes of entropy and enthalpy and the competition between a ligand and water molecules in molecular binding, which is known to be crucial for smart drug discovery.
Experiments provide measurements, however, computational methods provide information about binding/unbinding processes that allows for a complete picture of molecular recognition not directly available from experiments.
All the computed values of kon, koff, ΔH, ΔS, and ΔG use AMBER force fields for Protein-Protein and Protein-Ligand's interactions.
The experimental data is used to guide and improve the predictive, modeling tools for PROTAC drug discovery in iterative manner.
Roivant is using published PROTAC-bound ternary complexes, plus some data generated internally for the F@h projects, and all simulation data is being made publicly available. This is a project run by Roivant Sciences (formerly Silicon Therapeutics) as was officially announced in this press release: https://foldingathome.org/2021/04/20/maximizing-the-impact-of-foldinghome-by-engaging-industry-collaborators/.
RELATED TERMS GLOSSARY AI BETA
SMARCA2
A gene that encodes a protein involved in chromatin remodeling.
SMARCA2 is a gene that provides instructions for making a protein crucial for regulating how DNA is packaged and accessed within cells. This process is vital for many cellular functions, including cell growth and division. In cancer, mutations or alterations in SMARCA2 can contribute to uncontrolled cell proliferation and tumor development.
BRD4
A gene that encodes a protein involved in regulating gene expression.
BRD4 is a gene that produces instructions for making a protein that acts as a 'switch' for controlling gene activity. It plays a role in various cellular processes, including cell growth, differentiation, and inflammation. In cancer, BRD4 can be overexpressed or abnormally activated, contributing to tumor development and progression.
Bcl
A family of proteins involved in regulating apoptosis (programmed cell death).
Bcl proteins are a group of molecules that act like gatekeepers controlling whether a cell lives or dies. In healthy cells, they maintain a balance between cell survival and programmed death. However, in cancer cells, Bcl proteins can become dysregulated, promoting resistance to cell death and contributing to tumor growth.
BTK
A protein involved in B-cell receptor signaling.
BTK is a protein essential for the proper functioning of B cells, a type of white blood cell involved in the immune response. In certain cancers affecting B cells, such as chronic lymphocytic leukemia (CLL), BTK becomes overactive, driving uncontrolled cell growth. Inhibiting BTK has become a targeted therapy approach for these cancers.
PROTACs
PROteolysis TArgeting Chimeras
PROTACs are a novel class of drugs designed to degrade target proteins within cells. They function by recruiting an E3 ubiquitin ligase to bind to the desired protein, ultimately leading to its marking for destruction by cellular machinery. This approach offers advantages over traditional inhibitors by potentially achieving more potent and durable effects.
AMBER
A molecular simulation package used for studying biomolecular systems.
AMBER is a powerful computational tool widely used by researchers to simulate the behavior of molecules, including proteins and small drug-like compounds. It allows scientists to predict how molecules interact with each other, providing valuable insights for drug design and development.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:06|
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 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 5,172,378 | 408,555 | 12.66 | 1 hrs 54 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,473,258 | 390,022 | 11.47 | 2 hrs 6 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,036,827 | 381,000 | 10.60 | 2 hrs 16 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,579,051 | 363,552 | 9.84 | 2 hrs 26 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,317,085 | 355,927 | 9.32 | 2 hrs 35 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,033,670 | 345,289 | 8.79 | 2 hrs 44 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,530,537 | 325,839 | 7.77 | 3 hrs 5 mins |
| 8 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,512,027 | 325,291 | 7.72 | 3 hrs 6 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,379,869 | 319,153 | 7.46 | 3 hrs 13 mins |
| 10 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,379,854 | 320,859 | 7.42 | 3 hrs 14 mins |
| 11 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,252,179 | 313,669 | 7.18 | 3 hrs 21 mins |
| 12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,171,016 | 308,923 | 7.03 | 3 hrs 25 mins |
| 13 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,097,990 | 306,843 | 6.84 | 3 hrs 31 mins |
| 14 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,061,546 | 304,397 | 6.77 | 3 hrs 33 mins |
| 15 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,030,818 | 302,803 | 6.71 | 3 hrs 35 mins |
| 16 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,001,649 | 301,439 | 6.64 | 3 hrs 37 mins |
| 17 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,998,847 | 301,611 | 6.63 | 3 hrs 37 mins |
| 18 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,769,919 | 287,812 | 6.15 | 3 hrs 54 mins |
| 19 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,729,009 | 288,168 | 6.00 | 3 hrs 60 mins |
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|
|||||||
| 20 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 1,665,046 | 282,233 | 5.90 | 4 hrs 4 mins |
| 21 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,595,164 | 252,236 | 6.32 | 3 hrs 48 mins |
| 22 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,524,753 | 277,067 | 5.50 | 4 hrs 22 mins |
| 23 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,514,617 | 275,068 | 5.51 | 4 hrs 22 mins |
| 24 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,320,604 | 262,951 | 5.02 | 4 hrs 47 mins |
| 25 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,314,207 | 261,602 | 5.02 | 4 hrs 47 mins |
| 26 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,192,155 | 252,644 | 4.72 | 5 hrs 5 mins |
| 27 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,167,895 | 252,008 | 4.63 | 5 hrs 11 mins |
| 28 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,129,841 | 249,147 | 4.53 | 5 hrs 18 mins |
| 29 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,039,398 | 241,391 | 4.31 | 5 hrs 34 mins |
| 30 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 689,605 | 211,624 | 3.26 | 7 hrs 22 mins |
| 31 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 563,880 | 197,741 | 2.85 | 8 hrs 25 mins |
| 32 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 543,593 | 184,967 | 2.94 | 8 hrs 10 mins |
| 33 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 465,353 | 164,995 | 2.82 | 8 hrs 31 mins |
| 34 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 439,480 | 181,064 | 2.43 | 9 hrs 53 mins |
| 35 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 337,451 | 160,278 | 2.11 | 11 hrs 24 mins |
| 36 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 290,194 | 143,108 | 2.03 | 11 hrs 50 mins |
| 37 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 226,142 | 96,016 | 2.36 | 10 hrs 11 mins |
| 38 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 116,195 | 119,472 | 0.97 | 24 hrs 41 mins |
| 39 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 109,448 | 99,702 | 1.10 | 21 hrs 52 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:06|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|