PROJECT #18011 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

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/

PROJECT INFO

Manager(s): Rafal Wiewiora

Institution: Roivant Sciences (Silicon Therapeutics)

Project URL: roivant.com

PROJECT WORK UNIT SUMMARY

Atoms: 73,445

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Sunday, 17 October 2021 08:34:49

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 5,172,378 408,555 12.66 2 hrs 54 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,473,258 390,022 11.47 2 hrs 6 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 4,036,827 381,000 10.60 2 hrs 16 mins
4 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,579,051 363,552 9.84 2 hrs 26 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,317,085 355,927 9.32 3 hrs 35 mins
6 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,033,670 345,289 8.79 3 hrs 44 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,530,537 325,839 7.77 3 hrs 5 mins
8 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 2,512,027 325,291 7.72 3 hrs 6 mins
9 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,379,869 319,153 7.46 3 hrs 13 mins
10 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,379,854 320,859 7.42 3 hrs 14 mins
11 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,252,179 313,669 7.18 3 hrs 21 mins
12 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,171,016 308,923 7.03 3 hrs 25 mins
13 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,097,990 306,843 6.84 4 hrs 31 mins
14 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,061,546 304,397 6.77 4 hrs 33 mins
15 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,030,818 302,803 6.71 4 hrs 35 mins
16 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,001,649 301,439 6.64 4 hrs 37 mins
17 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 1,998,847 301,611 6.63 4 hrs 37 mins
18 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,769,919 287,812 6.15 4 hrs 54 mins
19 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,729,009 288,168 6.00 4 hrs 60 mins
20 Tesla P40
GP102GL [Tesla P40] 11760
Nvidia GP102GL 1,665,046 282,233 5.90 4 hrs 4 mins
21 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,595,164 252,236 6.32 4 hrs 48 mins
22 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,524,753 277,067 5.50 4 hrs 22 mins
23 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,514,617 275,068 5.51 4 hrs 22 mins
24 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,320,604 262,951 5.02 5 hrs 47 mins
25 GeForce RTX 3070 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,314,207 261,602 5.02 5 hrs 47 mins
26 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,192,155 252,644 4.72 5 hrs 5 mins
27 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,167,895 252,008 4.63 5 hrs 11 mins
28 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,129,841 249,147 4.53 5 hrs 18 mins
29 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,039,398 241,391 4.31 6 hrs 34 mins
30 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 689,605 211,624 3.26 7 hrs 22 mins
31 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 563,880 197,741 2.85 8 hrs 25 mins
32 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 543,593 184,967 2.94 8 hrs 10 mins
33 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 465,353 164,995 2.82 9 hrs 31 mins
34 P104-100
GP104 [P104-100]
Nvidia GP104 439,480 181,064 2.43 10 hrs 53 mins
35 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 337,451 160,278 2.11 11 hrs 24 mins
36 P106-100
GP106 [P106-100]
Nvidia GP106 290,194 143,108 2.03 12 hrs 50 mins
37 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 226,142 96,016 2.36 10 hrs 11 mins
38 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 116,195 119,472 0.97 25 hrs 41 mins
39 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 109,448 99,702 1.10 22 hrs 52 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 17 October 2021 08:34:49

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make