RESEARCH: CANCER
FOLDING PROJECT #18005 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 80,117Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project aims to find new cancer drugs that work even when tumors become resistant. They're using a special technology called PROTACs which breaks down harmful proteins instead of just blocking them. This approach is more effective and can target parts of proteins beyond just their active sites. The researchers use computer models to understand how these drugs work and are making their data public.
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 protein that regulates gene expression.
SMARCA2 is a protein involved in controlling how genes are turned on and off. It plays a role in various cellular processes and has been implicated in cancer development.
BRD4
Bromodomain-containing protein 4.
BRD4 is a protein that binds to specific DNA sequences and plays a role in regulating gene expression. It has been identified as a potential target for cancer therapy.
Bcl
B-cell lymphoma
Bcl refers to a group of proteins that regulate cell death. They are involved in various cellular processes and have been implicated in cancer development.
BTK
Bruton's tyrosine kinase
BTK is an enzyme involved in immune cell signaling. It plays a role in B-cell development and activation and has been targeted for cancer therapy.
PROTAC
Proteolysis TArgeting Chimeras
PROTACs are a new class of drugs that promote the degradation of target proteins. They work by recruiting an E3 ubiquitin ligase to bind to the target protein, marking it for destruction.
AMBER
Assisted Model Building with Energy Refinement.
AMBER is a widely used software package for simulating molecular interactions. It is commonly used in drug discovery to predict the binding affinity of molecules to their targets.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:15|
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 | 5,423,801 | 485,241 | 11.18 | 2 hrs 9 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,303,618 | 482,475 | 10.99 | 2 hrs 11 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,234,072 | 451,856 | 9.37 | 2 hrs 34 mins |
| 4 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,779,725 | 433,750 | 8.71 | 2 hrs 45 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,589,932 | 424,527 | 8.46 | 2 hrs 50 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,021,378 | 400,860 | 7.54 | 3 hrs 11 mins |
| 7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 2,883,693 | 396,637 | 7.27 | 3 hrs 18 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 2,739,307 | 358,574 | 7.64 | 3 hrs 8 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,518,486 | 379,540 | 6.64 | 3 hrs 37 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,453,441 | 374,422 | 6.55 | 3 hrs 40 mins |
| 11 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,356,098 | 371,286 | 6.35 | 3 hrs 47 mins |
| 12 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,251,841 | 366,711 | 6.14 | 3 hrs 55 mins |
| 13 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,220,275 | 364,338 | 6.09 | 3 hrs 56 mins |
| 14 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 2,168,641 | 360,002 | 6.02 | 3 hrs 59 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,135,922 | 359,379 | 5.94 | 4 hrs 2 mins |
| 16 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,018,026 | 352,938 | 5.72 | 4 hrs 12 mins |
| 17 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,994,089 | 351,316 | 5.68 | 4 hrs 14 mins |
| 18 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 1,966,770 | 350,046 | 5.62 | 4 hrs 16 mins |
| 19 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,929,997 | 347,546 | 5.55 | 4 hrs 19 mins |
|
|
|||||||
| 20 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 1,855,591 | 329,645 | 5.63 | 4 hrs 16 mins |
| 21 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,850,586 | 337,324 | 5.49 | 4 hrs 22 mins |
| 22 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 1,771,512 | 338,526 | 5.23 | 4 hrs 35 mins |
| 23 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,770,231 | 336,619 | 5.26 | 4 hrs 34 mins |
| 24 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,746,841 | 332,706 | 5.25 | 4 hrs 34 mins |
| 25 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,534,156 | 323,167 | 4.75 | 5 hrs 3 mins |
| 26 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,411,358 | 315,559 | 4.47 | 5 hrs 22 mins |
| 27 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,377,741 | 309,858 | 4.45 | 5 hrs 24 mins |
| 28 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,272,580 | 302,693 | 4.20 | 5 hrs 43 mins |
| 29 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,259,930 | 302,165 | 4.17 | 5 hrs 45 mins |
| 30 | Quadro P5000 GP104GL [Quadro P5000] |
Nvidia | GP104GL | 1,090,083 | 287,661 | 3.79 | 6 hrs 20 mins |
| 31 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,057,221 | 282,994 | 3.74 | 6 hrs 25 mins |
| 32 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,029,663 | 205,571 | 5.01 | 4 hrs 47 mins |
| 33 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 999,044 | 272,894 | 3.66 | 6 hrs 33 mins |
| 34 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 974,820 | 271,072 | 3.60 | 6 hrs 40 mins |
| 35 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 730,281 | 228,937 | 3.19 | 7 hrs 31 mins |
| 36 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 637,451 | 240,489 | 2.65 | 9 hrs 3 mins |
| 37 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 615,357 | 237,260 | 2.59 | 9 hrs 15 mins |
| 38 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 615,346 | 238,114 | 2.58 | 9 hrs 17 mins |
| 39 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 612,064 | 237,203 | 2.58 | 9 hrs 18 mins |
|
|
|||||||
| 40 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 557,205 | 229,792 | 2.42 | 9 hrs 54 mins |
| 41 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 509,792 | 210,889 | 2.42 | 9 hrs 56 mins |
| 42 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 460,702 | 202,648 | 2.27 | 10 hrs 33 mins |
| 43 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 427,206 | 201,424 | 2.12 | 11 hrs 19 mins |
| 44 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 397,553 | 204,229 | 1.95 | 12 hrs 20 mins |
| 45 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 337,028 | 194,520 | 1.73 | 13 hrs 51 mins |
| 46 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 292,174 | 185,753 | 1.57 | 15 hrs 15 mins |
| 47 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 267,705 | 179,566 | 1.49 | 16 hrs 6 mins |
| 48 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 218,454 | 149,879 | 1.46 | 16 hrs 28 mins |
| 49 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 91,889 | 120,988 | 0.76 | 31 hrs 36 mins |
| 50 | Ryzen vega 8 mobile Raven [Ryzen vega 8 mobile] |
AMD | Raven | 69,223 | 120,988 | 0.57 | 41 hrs 57 mins |
| 51 | Radeon RX Vega gfx902 raven [Radeon RX Vega gfx902] |
AMD | raven | 68,647 | 125,944 | 0.55 | 44 hrs 2 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:15|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|