PROJECT #18009 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: 68,820

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Tuesday, 26 October 2021 06:56:06

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 4,892,953 372,193 13.15 2 hrs 50 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,732,367 365,479 12.95 2 hrs 51 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 3,862,905 346,453 11.15 2 hrs 9 mins
4 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 3,443,838 309,743 11.12 2 hrs 10 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,111,287 318,939 9.76 2 hrs 28 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,053,228 318,301 9.59 3 hrs 30 mins
7 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 2,972,476 316,031 9.41 3 hrs 33 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,543,541 300,674 8.46 3 hrs 50 mins
9 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 2,472,810 298,442 8.29 3 hrs 54 mins
10 Quadro RTX 6000/8000
TU102GL [Quadro RTX 6000/8000]
Nvidia TU102GL 2,304,213 290,697 7.93 3 hrs 2 mins
11 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,166,675 285,681 7.58 3 hrs 10 mins
12 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,100,379 281,591 7.46 3 hrs 13 mins
13 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,005,800 279,241 7.18 3 hrs 20 mins
14 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 1,998,278 277,570 7.20 3 hrs 20 mins
15 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 1,914,690 274,224 6.98 3 hrs 26 mins
16 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 1,914,116 274,195 6.98 3 hrs 26 mins
17 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 1,879,538 272,925 6.89 3 hrs 29 mins
18 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 1,843,782 272,224 6.77 4 hrs 33 mins
19 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,733,262 264,641 6.55 4 hrs 40 mins
20 Tesla P40
GP102GL [Tesla P40] 11760
Nvidia GP102GL 1,626,979 258,784 6.29 4 hrs 49 mins
21 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,606,978 258,861 6.21 4 hrs 52 mins
22 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,534,911 255,355 6.01 4 hrs 60 mins
23 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,505,171 253,051 5.95 4 hrs 2 mins
24 GeForce RTX 3070 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,436,702 248,856 5.77 4 hrs 9 mins
25 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,391,730 246,145 5.65 4 hrs 15 mins
26 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,268,936 239,058 5.31 5 hrs 31 mins
27 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,101,805 227,872 4.84 5 hrs 58 mins
28 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,064,582 226,314 4.70 5 hrs 6 mins
29 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 915,119 202,503 4.52 5 hrs 19 mins
30 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 567,252 182,749 3.10 8 hrs 44 mins
31 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 554,252 157,399 3.52 7 hrs 49 mins
32 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 536,622 179,347 2.99 8 hrs 1 mins
33 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 460,117 170,576 2.70 9 hrs 54 mins
34 P104-100
GP104 [P104-100]
Nvidia GP104 403,856 163,460 2.47 10 hrs 43 mins
35 Quadro T2000 Mobile / Max-Q
TU117GLM [Quadro T2000 Mobile / Max-Q]
Nvidia TU117GLM 403,005 163,089 2.47 10 hrs 43 mins
36 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 383,269 157,275 2.44 10 hrs 51 mins
37 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 330,329 152,438 2.17 11 hrs 5 mins
38 P106-100
GP106 [P106-100]
Nvidia GP106 313,300 150,052 2.09 11 hrs 30 mins
39 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 226,118 137,436 1.65 15 hrs 35 mins
40 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 156,066 85,121 1.83 13 hrs 5 mins
41 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 91,772 88,051 1.04 23 hrs 2 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Tuesday, 26 October 2021 06:56:06

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