PROJECT #18022 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/

All data is being made publicly available in real time at https://console.cloud.google.com/storage/browser/stxfah-bucket

PROJECT INFO

Manager(s): Rafal Wiewiora

Institution: Roivant Sciences (Silicon Therapeutics)

Project URL: roivant.com

PROJECT WORK UNIT SUMMARY

Atoms: 69,696

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 27 September 2021 14:58:25

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 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,348,780 481,037 13.20 2 hrs 49 mins
2 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 6,101,232 477,033 12.79 2 hrs 53 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 5,956,049 473,158 12.59 2 hrs 54 mins
4 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,649,738 466,152 12.12 2 hrs 59 mins
5 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,796,904 435,491 11.01 2 hrs 11 mins
6 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,513,420 433,002 10.42 2 hrs 18 mins
7 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,359,281 426,884 10.21 2 hrs 21 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,620,766 402,035 9.01 3 hrs 40 mins
9 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,433,751 395,308 8.69 3 hrs 46 mins
10 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,925,505 374,694 7.81 3 hrs 4 mins
11 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,817,177 369,727 7.62 3 hrs 9 mins
12 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,398,116 351,395 6.82 4 hrs 31 mins
13 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,339,958 347,275 6.74 4 hrs 34 mins
14 Tesla P40
GP102GL [Tesla P40] 11760
Nvidia GP102GL 2,109,284 334,443 6.31 4 hrs 48 mins
15 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,979,553 326,793 6.06 4 hrs 58 mins
16 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,853,313 310,679 5.97 4 hrs 1 mins
17 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,828,998 320,172 5.71 4 hrs 12 mins
18 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,631,758 316,196 5.16 5 hrs 39 mins
19 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,616,588 305,472 5.29 5 hrs 32 mins
20 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,603,453 306,602 5.23 5 hrs 35 mins
21 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 1,600,105 306,120 5.23 5 hrs 35 mins
22 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,431,460 266,698 5.37 4 hrs 28 mins
23 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,406,233 294,040 4.78 5 hrs 1 mins
24 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,169,831 258,976 4.52 5 hrs 19 mins
25 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 1,060,937 265,965 3.99 6 hrs 1 mins
26 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 914,287 244,918 3.73 6 hrs 26 mins
27 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 875,101 250,679 3.49 7 hrs 52 mins
28 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 728,076 235,624 3.09 8 hrs 46 mins
29 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 661,765 228,170 2.90 8 hrs 16 mins
30 GeForce GTX 1050 Ti Mobile
GP107M [GeForce GTX 1050 Ti Mobile]
Nvidia GP107M 303,781 177,216 1.71 14 hrs 0 mins
31 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 248,495 138,539 1.79 13 hrs 23 mins
32 Radeon RX Vega M XL
[Radeon RX Vega M XL]
AMD Vega 190,812 143,043 1.33 18 hrs 60 mins
33 Radeon HD 7800 Series
Pitcairn PRO [Radeon HD 7800 Series]
AMD Pitcairn PRO 103,666 110,000 0.94 25 hrs 28 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 27 September 2021 14:58:25

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