RESEARCH: CANCER
FOLDING PROJECT #18004 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 63,447Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at new cancer drugs called PROTACs that destroy cancer-causing proteins instead of just blocking them. This method could help overcome drug resistance, which is a big problem in cancer treatment. The project uses computer modeling to understand how these drugs work and improve their design.
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 involved in chromatin remodeling.
SMARCA2 is a gene that produces a protein important for controlling how tightly DNA is packaged. This packaging affects how genes are turned on or off, playing a role in cell growth and development.
BRD4
A protein involved in gene regulation.
BRD4 is a protein that helps control which genes are active in a cell. It plays a role in various cellular processes, including cell growth, division, and immune responses.
Bcl
A family of proteins involved in cell survival and death.
Bcl refers to a group of proteins that regulate programmed cell death (apoptosis). Some Bcl proteins promote cell survival, while others trigger cell death. They are crucial for maintaining healthy cell populations.
BTK
A protein involved in immune cell signaling.
BTK is a protein that plays a vital role in the activation and function of certain immune cells. It's involved in transmitting signals that lead to an immune response against pathogens.
PROTAC
PROteolysis TArgeting Chimeras
PROTACs are a novel class of drugs that work by degrading target proteins. They consist of two parts: a ligand that binds to the protein of interest and a recruiter molecule that tags it for destruction.
AMBER
A software package for molecular dynamics simulations.
AMBER is a widely used computer program that simulates the movement and interactions of atoms in molecules. It's essential for understanding how drugs interact with their targets.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:17|
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 | 5,136,636 | 349,713 | 14.69 | 1 hrs 38 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,076,154 | 349,860 | 14.51 | 1 hrs 39 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,853,248 | 322,828 | 11.94 | 2 hrs 1 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,313,504 | 306,135 | 10.82 | 2 hrs 13 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,246,900 | 303,354 | 10.70 | 2 hrs 15 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 2,816,326 | 289,560 | 9.73 | 2 hrs 28 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,571,088 | 282,172 | 9.11 | 2 hrs 38 mins |
| 8 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,522,592 | 280,345 | 9.00 | 2 hrs 40 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,362,676 | 273,607 | 8.64 | 2 hrs 47 mins |
| 10 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,240,633 | 269,738 | 8.31 | 2 hrs 53 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,197,444 | 267,029 | 8.23 | 2 hrs 55 mins |
| 12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,161,227 | 265,617 | 8.14 | 2 hrs 57 mins |
| 13 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,052,166 | 261,667 | 7.84 | 3 hrs 4 mins |
| 14 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 1,959,169 | 257,974 | 7.59 | 3 hrs 10 mins |
| 15 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,921,726 | 256,014 | 7.51 | 3 hrs 12 mins |
| 16 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,868,008 | 254,021 | 7.35 | 3 hrs 16 mins |
| 17 | TITAN X GP102 [TITAN X] 6144 |
Nvidia | GP102 | 1,828,266 | 251,785 | 7.26 | 3 hrs 18 mins |
| 18 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,766,633 | 248,304 | 7.11 | 3 hrs 22 mins |
| 19 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 1,740,953 | 247,505 | 7.03 | 3 hrs 25 mins |
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|
|||||||
| 20 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,721,988 | 246,135 | 7.00 | 3 hrs 26 mins |
| 21 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,661,906 | 244,147 | 6.81 | 3 hrs 32 mins |
| 22 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,659,367 | 244,093 | 6.80 | 3 hrs 32 mins |
| 23 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 1,600,436 | 240,806 | 6.65 | 3 hrs 37 mins |
| 24 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,450,496 | 232,497 | 6.24 | 3 hrs 51 mins |
| 25 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,421,395 | 231,220 | 6.15 | 3 hrs 54 mins |
| 26 | Quadro P5000 GP104GL [Quadro P5000] |
Nvidia | GP104GL | 1,378,171 | 229,695 | 6.00 | 3 hrs 60 mins |
| 27 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,373,614 | 229,362 | 5.99 | 4 hrs 0 mins |
| 28 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,369,977 | 229,030 | 5.98 | 4 hrs 1 mins |
| 29 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,162,464 | 215,741 | 5.39 | 4 hrs 27 mins |
| 30 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,061,333 | 209,765 | 5.06 | 4 hrs 45 mins |
| 31 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 920,813 | 200,430 | 4.59 | 5 hrs 13 mins |
| 32 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 741,326 | 185,613 | 3.99 | 6 hrs 1 mins |
| 33 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 716,303 | 184,173 | 3.89 | 6 hrs 10 mins |
| 34 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 630,102 | 176,294 | 3.57 | 6 hrs 43 mins |
| 35 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 620,574 | 175,439 | 3.54 | 6 hrs 47 mins |
| 36 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 489,168 | 155,308 | 3.15 | 7 hrs 37 mins |
| 37 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 481,053 | 161,371 | 2.98 | 8 hrs 3 mins |
| 38 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 426,861 | 156,008 | 2.74 | 8 hrs 46 mins |
| 39 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 402,435 | 152,263 | 2.64 | 9 hrs 5 mins |
|
|
|||||||
| 40 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 397,737 | 150,097 | 2.65 | 9 hrs 3 mins |
| 41 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 269,946 | 133,239 | 2.03 | 11 hrs 51 mins |
| 42 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 268,576 | 123,182 | 2.18 | 11 hrs 0 mins |
| 43 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 107,920 | 98,144 | 1.10 | 21 hrs 50 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:17|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|