PROJECT #18026 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: 119,082

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 6,869,748 414,330 16.58 1 hrs 27 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,696,858 413,783 16.18 1 hrs 29 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,196,625 404,286 15.33 2 hrs 34 mins
4 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,897,338 394,175 14.96 2 hrs 36 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,547,897 364,460 12.48 2 hrs 55 mins
6 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,510,603 364,078 12.39 2 hrs 56 mins
7 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,224,107 354,163 11.93 2 hrs 1 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,771,436 342,459 11.01 2 hrs 11 mins
9 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,482,037 334,469 10.41 2 hrs 18 mins
10 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 3,034,835 450,697 6.73 4 hrs 34 mins
11 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 3,005,796 317,494 9.47 3 hrs 32 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 2,868,632 312,754 9.17 3 hrs 37 mins
13 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,836,930 312,072 9.09 3 hrs 38 mins
14 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,658,665 304,209 8.74 3 hrs 45 mins
15 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,386,677 294,233 8.11 3 hrs 58 mins
16 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,179,770 282,798 7.71 3 hrs 7 mins
17 Tesla P40
GP102GL [Tesla P40] 11760
Nvidia GP102GL 1,941,340 274,761 7.07 3 hrs 24 mins
18 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,870,568 271,279 6.90 3 hrs 29 mins
19 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,571,721 255,828 6.14 4 hrs 54 mins
20 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 1,549,797 252,681 6.13 4 hrs 55 mins
21 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,528,930 253,986 6.02 4 hrs 59 mins
22 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,422,901 248,049 5.74 4 hrs 11 mins
23 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,396,214 246,836 5.66 4 hrs 15 mins
24 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 1,314,291 241,474 5.44 4 hrs 25 mins
25 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,247,791 235,792 5.29 5 hrs 32 mins
26 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 969,641 218,208 4.44 5 hrs 24 mins
27 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 827,180 206,823 4.00 6 hrs 0 mins
28 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 651,757 191,175 3.41 7 hrs 2 mins
29 Quadro T2000 Mobile / Max-Q
TU117GLM [Quadro T2000 Mobile / Max-Q]
Nvidia TU117GLM 489,591 174,350 2.81 9 hrs 33 mins
30 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 426,362 138,598 3.08 8 hrs 48 mins
31 P106-090
GP106 [P106-090]
Nvidia GP106 314,151 150,049 2.09 11 hrs 28 mins
32 Radeon RX Vega M XL
[Radeon RX Vega M XL]
AMD Vega 186,047 110,203 1.69 14 hrs 13 mins
33 Radeon HD 7800 Series
Pitcairn PRO [Radeon HD 7800 Series]
AMD Pitcairn PRO 138,823 114,561 1.21 20 hrs 48 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