RESEARCH: CANCER
FOLDING PROJECT #18025 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 234,724Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores new cancer drugs that work by destroying target proteins instead of blocking them. This method could overcome drug resistance and affect more parts of a protein than traditional drugs. Researchers use computer simulations to understand how these drugs work and make better ones.
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
SMARCA2
A protein involved in chromatin remodeling.
SMARCA2 is a gene that codes for a protein essential for regulating DNA accessibility. Alterations in SMARCA2 can contribute to cancer development by affecting how genes are expressed.
BRD4
A protein involved in gene regulation and cell proliferation.
BRD4 is a gene that codes for a protein playing a crucial role in regulating gene expression. Its overexpression can contribute to cancer growth by promoting uncontrolled cell division.
Bcl
A family of proteins involved in apoptosis (cell death).
Bcl refers to a group of genes that produce proteins influencing programmed cell death. Some Bcl proteins promote cell survival, while others trigger apoptosis, and their balance is crucial for normal cellular function.
BTK
Bruton's tyrosine kinase
BTK is an enzyme crucial for immune cell signaling. Its dysregulation can lead to certain types of cancers, making it a target for therapy.
PROTACs
PROteolysis TArgeting Chimeras
PROTACs are a novel class of drugs that induce protein degradation by hijacking the cell's natural protein recycling system. They offer a promising alternative to traditional inhibitors for treating various diseases.
AMBER
A software package for molecular simulations.
AMBER is a widely used computer program that simulates the behavior of molecules at the atomic level. It's essential for understanding how drugs interact with their targets and predicting their effectiveness.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:32:46|
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 | 7,390,316 | 425,601 | 17.36 | 1 hrs 23 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,149,422 | 420,268 | 17.01 | 1 hrs 25 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,302,122 | 406,465 | 15.50 | 1 hrs 33 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,782,964 | 388,576 | 14.88 | 1 hrs 37 mins |
| 5 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 4,865,832 | 368,637 | 13.20 | 1 hrs 49 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,626,895 | 365,942 | 12.64 | 1 hrs 54 mins |
| 7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,595,047 | 365,022 | 12.59 | 1 hrs 54 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,556,107 | 364,421 | 12.50 | 1 hrs 55 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,699,076 | 340,223 | 10.87 | 2 hrs 12 mins |
| 10 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,281,627 | 327,394 | 10.02 | 2 hrs 24 mins |
| 11 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,272,620 | 326,732 | 10.02 | 2 hrs 24 mins |
| 12 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,234,475 | 325,700 | 9.93 | 2 hrs 25 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 3,190,016 | 508,551 | 6.27 | 3 hrs 50 mins |
| 14 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 3,137,193 | 322,400 | 9.73 | 2 hrs 28 mins |
| 15 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 3,026,850 | 318,728 | 9.50 | 2 hrs 32 mins |
| 16 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,785,670 | 310,839 | 8.96 | 2 hrs 41 mins |
| 17 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 2,773,214 | 309,777 | 8.95 | 2 hrs 41 mins |
| 18 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,766,694 | 308,764 | 8.96 | 2 hrs 41 mins |
| 19 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,765,417 | 309,612 | 8.93 | 2 hrs 41 mins |
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| 20 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,600,651 | 303,117 | 8.58 | 2 hrs 48 mins |
| 21 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,591,695 | 302,534 | 8.57 | 2 hrs 48 mins |
| 22 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,340,288 | 291,496 | 8.03 | 2 hrs 59 mins |
| 23 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,338,020 | 292,635 | 7.99 | 3 hrs 0 mins |
| 24 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,328,944 | 290,619 | 8.01 | 2 hrs 60 mins |
| 25 | TITAN X GP102 [TITAN X] 6144 |
Nvidia | GP102 | 2,274,736 | 289,134 | 7.87 | 3 hrs 3 mins |
| 26 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 2,174,714 | 284,803 | 7.64 | 3 hrs 9 mins |
| 27 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 2,174,214 | 282,675 | 7.69 | 3 hrs 7 mins |
| 28 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 1,882,257 | 271,040 | 6.94 | 3 hrs 27 mins |
| 29 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,874,579 | 271,618 | 6.90 | 3 hrs 29 mins |
| 30 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,689,045 | 250,105 | 6.75 | 3 hrs 33 mins |
| 31 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,614,203 | 248,752 | 6.49 | 3 hrs 42 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 | 1,535,003 | 251,618 | 6.10 | 3 hrs 56 mins |
| 33 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,487,855 | 251,702 | 5.91 | 4 hrs 4 mins |
| 34 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 1,478,896 | 249,033 | 5.94 | 4 hrs 2 mins |
| 35 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,394,359 | 246,106 | 5.67 | 4 hrs 14 mins |
| 36 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,363,835 | 239,548 | 5.69 | 4 hrs 13 mins |
| 37 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,276,356 | 236,533 | 5.40 | 4 hrs 27 mins |
| 38 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,239,001 | 236,908 | 5.23 | 4 hrs 35 mins |
| 39 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,206,450 | 233,537 | 5.17 | 4 hrs 39 mins |
|
|
|||||||
| 40 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,129,791 | 226,034 | 5.00 | 4 hrs 48 mins |
| 41 | RTX A2000 Mobile GA107GLM [RTX A2000 Mobile] |
Nvidia | GA107GLM | 982,158 | 219,804 | 4.47 | 5 hrs 22 mins |
| 42 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 969,943 | 218,277 | 4.44 | 5 hrs 24 mins |
| 43 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 949,758 | 217,329 | 4.37 | 5 hrs 30 mins |
| 44 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 892,575 | 212,374 | 4.20 | 5 hrs 43 mins |
| 45 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 868,336 | 208,743 | 4.16 | 5 hrs 46 mins |
| 46 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 797,345 | 204,457 | 3.90 | 6 hrs 9 mins |
| 47 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 776,832 | 202,558 | 3.84 | 6 hrs 15 mins |
| 48 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 768,307 | 201,557 | 3.81 | 6 hrs 18 mins |
| 49 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 742,795 | 197,659 | 3.76 | 6 hrs 23 mins |
| 50 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 700,733 | 195,931 | 3.58 | 6 hrs 43 mins |
| 51 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 634,584 | 189,294 | 3.35 | 7 hrs 10 mins |
| 52 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 596,148 | 185,512 | 3.21 | 7 hrs 28 mins |
| 53 | Radeon RX 5500/5500M / Pro 5500M Navi 14 [Radeon RX 5500/5500M / Pro 5500M] |
AMD | Navi 14 | 595,289 | 185,423 | 3.21 | 7 hrs 29 mins |
| 54 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 544,554 | 179,653 | 3.03 | 7 hrs 55 mins |
| 55 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 509,126 | 176,669 | 2.88 | 8 hrs 20 mins |
| 56 | Radeon R9 280/HD 7900/8950 Tahiti PRO [Radeon R9 280/HD 7900/8950] |
AMD | Tahiti PRO | 488,276 | 172,931 | 2.82 | 8 hrs 30 mins |
| 57 | Radeon R9 200/HD 7900/8970 Tahiti XT [Radeon R9 200/HD 7900/8970] |
AMD | Tahiti XT | 414,291 | 152,962 | 2.71 | 8 hrs 52 mins |
| 58 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 352,693 | 155,894 | 2.26 | 10 hrs 36 mins |
| 59 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 352,204 | 155,953 | 2.26 | 10 hrs 38 mins |
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|||||||
| 60 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 329,030 | 152,218 | 2.16 | 11 hrs 6 mins |
| 61 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 308,573 | 148,977 | 2.07 | 11 hrs 35 mins |
| 62 | GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile] |
Nvidia | GP107M | 292,330 | 136,498 | 2.14 | 11 hrs 12 mins |
| 63 | Radeon R9 200 Series Hawaii [Radeon R9 200 Series] |
AMD | Hawaii | 248,111 | 138,410 | 1.79 | 13 hrs 23 mins |
| 64 | Radeon HD 7800 Pitcairn [Radeon HD 7800] |
AMD | Pitcairn | 233,969 | 135,860 | 1.72 | 13 hrs 56 mins |
| 65 | Radeon RX Vega M XL [Radeon RX Vega M XL] |
AMD | Vega | 212,917 | 117,166 | 1.82 | 13 hrs 12 mins |
| 66 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 158,190 | 119,246 | 1.33 | 18 hrs 5 mins |
| 67 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 133,919 | 96,356 | 1.39 | 17 hrs 16 mins |
| 68 | Radeon RX Vega gfx902 raven [Radeon RX Vega gfx902] |
AMD | raven | 63,234 | 85,000 | 0.74 | 32 hrs 16 mins |
| 69 | FirePro W2100 Oland GL [FirePro W2100] |
AMD | Oland GL | 20,406 | 85,000 | 0.24 | 99 hrs 58 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:32:46|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|