RESEARCH: CANCER
FOLDING PROJECT #18004 PROFILE

PROJECT TEAM

Manager(s): Rafal Wiewiora
Institution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website

WORK UNIT INFO

Atoms: 63,447
Core: OPENMM_22
Status: Public

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

Note: Glossary items are a high level summary and may not be 100% accurate.

SMARCA2

A protein involved in chromatin remodeling.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

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.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

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.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

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.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

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

Acronym: Pharmaceutical
Biotechnology / Drug Discovery

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.

Technical: Pharmaceutical
Biotechnology / Drug Discovery

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
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