PROJECT #18008 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: 84,624
Core: OPENMM_22
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Tuesday, 07 February 2023 12:14:55
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 | 5,705,962 | 489,007 | 11.67 | 2 hrs 3 mins |
2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,917,874 | 442,126 | 11.12 | 2 hrs 9 mins |
3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,357,059 | 448,052 | 9.72 | 2 hrs 28 mins |
4 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,474,597 | 415,760 | 8.36 | 3 hrs 52 mins |
5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,443,679 | 412,190 | 8.35 | 3 hrs 52 mins |
6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,354,752 | 410,119 | 8.18 | 3 hrs 56 mins |
7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,112,452 | 399,950 | 7.78 | 3 hrs 5 mins |
8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,617,305 | 378,521 | 6.91 | 3 hrs 28 mins |
9 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,511,236 | 373,472 | 6.72 | 4 hrs 34 mins |
10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,459,487 | 370,219 | 6.64 | 4 hrs 37 mins |
11 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,320,525 | 363,484 | 6.38 | 4 hrs 46 mins |
12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,302,713 | 363,143 | 6.34 | 4 hrs 47 mins |
13 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,057,998 | 349,689 | 5.89 | 4 hrs 5 mins |
14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,054,216 | 348,710 | 5.89 | 4 hrs 4 mins |
15 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,039,622 | 349,098 | 5.84 | 4 hrs 6 mins |
16 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,019,471 | 347,480 | 5.81 | 4 hrs 8 mins |
17 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,995,676 | 346,439 | 5.76 | 4 hrs 10 mins |
18 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,930,337 | 341,807 | 5.65 | 4 hrs 15 mins |
19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,817,795 | 333,580 | 5.45 | 4 hrs 24 mins |
20 | Tesla P40 GP102GL [Tesla P40] 11760 |
Nvidia | GP102GL | 1,686,804 | 326,562 | 5.17 | 5 hrs 39 mins |
21 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,555,796 | 310,510 | 5.01 | 5 hrs 47 mins |
22 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 1,494,380 | 314,182 | 4.76 | 5 hrs 3 mins |
23 | GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,480,150 | 309,695 | 4.78 | 5 hrs 1 mins |
24 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,430,320 | 307,078 | 4.66 | 5 hrs 9 mins |
25 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,397,629 | 304,830 | 4.58 | 5 hrs 14 mins |
26 | Quadro P5000 GP104GL [Quadro P5000] |
Nvidia | GP104GL | 1,365,853 | 305,104 | 4.48 | 5 hrs 22 mins |
27 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,222,586 | 293,043 | 4.17 | 6 hrs 45 mins |
28 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,196,198 | 291,930 | 4.10 | 6 hrs 51 mins |
29 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 994,767 | 264,279 | 3.76 | 6 hrs 23 mins |
30 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 985,651 | 236,762 | 4.16 | 6 hrs 46 mins |
31 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 917,379 | 259,815 | 3.53 | 7 hrs 48 mins |
32 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 814,017 | 230,280 | 3.53 | 7 hrs 47 mins |
33 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 708,364 | 244,820 | 2.89 | 8 hrs 18 mins |
34 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 611,640 | 233,612 | 2.62 | 9 hrs 10 mins |
35 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 574,543 | 225,384 | 2.55 | 9 hrs 25 mins |
36 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 510,939 | 219,679 | 2.33 | 10 hrs 19 mins |
37 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 464,999 | 212,901 | 2.18 | 11 hrs 59 mins |
38 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 444,250 | 199,060 | 2.23 | 11 hrs 45 mins |
39 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 423,629 | 200,354 | 2.11 | 11 hrs 21 mins |
40 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 332,315 | 190,544 | 1.74 | 14 hrs 46 mins |
41 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 305,971 | 185,406 | 1.65 | 15 hrs 33 mins |
42 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 246,194 | 162,301 | 1.52 | 16 hrs 49 mins |
43 | Radeon RX Vega M XL [Radeon RX Vega M XL] |
AMD | Vega | 179,590 | 149,079 | 1.20 | 20 hrs 55 mins |
44 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 104,590 | 119,958 | 0.87 | 28 hrs 32 mins |
45 | Radeon R7 250/HD 7700 R575A [Radeon R7 250/HD 7700] |
AMD | R575A | 52,949 | 118,154 | 0.45 | 54 hrs 33 mins |
46 | Radeon RX Vega gfx902 raven [Radeon RX Vega gfx902] |
AMD | raven | 32,502 | 118,154 | 0.28 | 87 hrs 15 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
PPDDB data as of Tuesday, 07 February 2023 12:14:55
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|