RESEARCH: CANCER
FOLDING PROJECT #18022 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 69,696Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project investigates new anti-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 a wider range of protein functions. Researchers use computer simulations to design PROTACs and are making their 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/ All data is being made publicly available in real time at https://console.cloud.google.com/storage/browser/stxfah-bucket.
RELATED TERMS GLOSSARY AI BETA
oncogenes
Genes that can cause cancer to develop.
Oncogenes are genes that have the potential to cause cancer. When they become mutated or overactive, they can promote uncontrolled cell growth and division, leading to tumor formation.
SMARCA2
SWI/SNF-related matrix-associated, actin-dependent regulator of chromatin, subfamily A member 2
SMARCA2 is a protein that plays a role in regulating gene expression. It is involved in chromatin remodeling, which is the process of altering the structure of DNA and its associated proteins to control gene activity. Mutations in SMARCA2 have been linked to various cancers.
BRD4
Bromodomain-containing protein 4
BRD4 is a protein that binds to acetylated histones, which are proteins associated with DNA. BRD4 plays a role in gene regulation and has been implicated in the development of various cancers.
Bcl
B-cell lymphoma 2 protein family
Bcl proteins are a family of proteins that play a role in regulating apoptosis, or programmed cell death. Some Bcl proteins promote cell survival, while others induce apoptosis.
BTK
Bruton's tyrosine kinase
BTK is a protein that plays a crucial role in the development and function of B cells, a type of white blood cell involved in the immune response.
PROTACs
PROteolysis TArgeting Chimeras
PROTACs are a novel class of drugs that work by targeting proteins for degradation. They consist of two parts: a ligand that binds to the target protein and a recruiter that binds to an E3 ligase, which is responsible for tagging proteins for degradation.
AMBER
Assisted Model Building with Energy Refinement
AMBER is a widely used software package for molecular modeling and simulations. It is used to study the structure, dynamics, and interactions of biomolecules.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:51|
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 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,506,474 | 484,853 | 13.42 | 1 hrs 47 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,308,577 | 478,966 | 13.17 | 1 hrs 49 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,782,053 | 466,941 | 12.38 | 1 hrs 56 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,454,538 | 453,177 | 12.04 | 1 hrs 60 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,665,666 | 430,407 | 10.84 | 2 hrs 13 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,285,698 | 424,317 | 10.10 | 2 hrs 23 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,205,907 | 422,260 | 9.96 | 2 hrs 25 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,624,208 | 400,709 | 9.04 | 2 hrs 39 mins |
| 9 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,464,114 | 396,160 | 8.74 | 2 hrs 45 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,372,423 | 388,826 | 8.67 | 2 hrs 46 mins |
| 11 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 3,093,682 | 380,892 | 8.12 | 2 hrs 57 mins |
| 12 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 3,079,034 | 380,809 | 8.09 | 2 hrs 58 mins |
| 13 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 3,035,711 | 374,639 | 8.10 | 2 hrs 58 mins |
| 14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,943,558 | 374,899 | 7.85 | 3 hrs 3 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,923,130 | 374,523 | 7.80 | 3 hrs 4 mins |
| 16 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 2,721,116 | 366,900 | 7.42 | 3 hrs 14 mins |
| 17 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,638,524 | 360,053 | 7.33 | 3 hrs 17 mins |
| 18 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 2,487,817 | 351,069 | 7.09 | 3 hrs 23 mins |
| 19 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,457,042 | 353,672 | 6.95 | 3 hrs 27 mins |
|
|
|||||||
| 20 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,370,955 | 349,240 | 6.79 | 3 hrs 32 mins |
| 21 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 2,109,284 | 334,443 | 6.31 | 3 hrs 48 mins |
| 22 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,080,744 | 333,069 | 6.25 | 3 hrs 51 mins |
| 23 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,044,736 | 319,802 | 6.39 | 3 hrs 45 mins |
| 24 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,875,291 | 315,798 | 5.94 | 4 hrs 2 mins |
| 25 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,820,422 | 319,828 | 5.69 | 4 hrs 13 mins |
| 26 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,635,707 | 304,065 | 5.38 | 4 hrs 28 mins |
| 27 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,631,758 | 316,196 | 5.16 | 4 hrs 39 mins |
| 28 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,616,588 | 305,472 | 5.29 | 4 hrs 32 mins |
| 29 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,600,129 | 306,443 | 5.22 | 4 hrs 36 mins |
| 30 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 1,561,480 | 303,375 | 5.15 | 4 hrs 40 mins |
| 31 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,421,659 | 294,365 | 4.83 | 4 hrs 58 mins |
| 32 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,380,134 | 273,976 | 5.04 | 4 hrs 46 mins |
| 33 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,322,635 | 287,465 | 4.60 | 5 hrs 13 mins |
| 34 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,321,095 | 287,314 | 4.60 | 5 hrs 13 mins |
| 35 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] 4550 |
Nvidia | TU106M | 1,260,297 | 283,710 | 4.44 | 5 hrs 24 mins |
| 36 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,249,857 | 277,177 | 4.51 | 5 hrs 19 mins |
| 37 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 1,094,645 | 265,959 | 4.12 | 5 hrs 50 mins |
| 38 | RTX A2000 Mobile GA107GLM [RTX A2000 Mobile] |
Nvidia | GA107GLM | 988,999 | 261,747 | 3.78 | 6 hrs 21 mins |
| 39 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 951,323 | 257,464 | 3.69 | 6 hrs 30 mins |
|
|
|||||||
| 40 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 933,892 | 247,876 | 3.77 | 6 hrs 22 mins |
| 41 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 915,931 | 249,160 | 3.68 | 6 hrs 32 mins |
| 42 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 875,101 | 250,679 | 3.49 | 6 hrs 52 mins |
| 43 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 866,671 | 250,142 | 3.46 | 6 hrs 56 mins |
| 44 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 778,157 | 240,245 | 3.24 | 7 hrs 25 mins |
| 45 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 752,575 | 238,266 | 3.16 | 7 hrs 36 mins |
| 46 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 728,076 | 235,624 | 3.09 | 7 hrs 46 mins |
| 47 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 712,460 | 233,915 | 3.05 | 7 hrs 53 mins |
| 48 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 539,182 | 213,391 | 2.53 | 9 hrs 30 mins |
| 49 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 533,985 | 213,050 | 2.51 | 9 hrs 35 mins |
| 50 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 517,877 | 204,931 | 2.53 | 9 hrs 30 mins |
| 51 | Radeon RX 5500/5500M / Pro 5500M Navi 14 [Radeon RX 5500/5500M / Pro 5500M] |
AMD | Navi 14 | 517,377 | 210,947 | 2.45 | 9 hrs 47 mins |
| 52 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 512,479 | 197,492 | 2.59 | 9 hrs 15 mins |
| 53 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 385,634 | 189,914 | 2.03 | 11 hrs 49 mins |
| 54 | Radeon R9 200/HD 7900/8970 Tahiti XT [Radeon R9 200/HD 7900/8970] |
AMD | Tahiti XT | 328,072 | 172,664 | 1.90 | 12 hrs 38 mins |
| 55 | GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile] |
Nvidia | GP107M | 303,781 | 177,216 | 1.71 | 14 hrs 0 mins |
| 56 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 294,366 | 165,455 | 1.78 | 13 hrs 29 mins |
| 57 | Radeon R9 200 Series Hawaii [Radeon R9 200 Series] |
AMD | Hawaii | 200,324 | 153,359 | 1.31 | 18 hrs 22 mins |
| 58 | Radeon RX Vega M XL [Radeon RX Vega M XL] |
AMD | Vega | 195,600 | 145,465 | 1.34 | 17 hrs 51 mins |
| 59 | Radeon HD 7800 Pitcairn [Radeon HD 7800] |
AMD | Pitcairn | 184,071 | 146,015 | 1.26 | 19 hrs 2 mins |
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| 60 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 151,836 | 139,737 | 1.09 | 22 hrs 5 mins |
| 61 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 107,994 | 110,000 | 0.98 | 24 hrs 27 mins |
| 62 | Radeon HD 7800 Series Pitcairn PRO [Radeon HD 7800 Series] |
AMD | Pitcairn PRO | 103,666 | 110,000 | 0.94 | 25 hrs 28 mins |
| 63 | Radeon RX Vega gfx902 raven [Radeon RX Vega gfx902] |
AMD | raven | 75,654 | 110,000 | 0.69 | 34 hrs 54 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:51|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|