PROJECT #18013 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: 49,887
Core: OPENMM_22
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Tuesday, 31 January 2023 00:15:00
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 | 4,840,172 | 272,125 | 17.79 | 1 hrs 21 mins |
2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,633,135 | 267,527 | 17.32 | 1 hrs 23 mins |
3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,613,684 | 248,163 | 14.56 | 2 hrs 39 mins |
4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,285,123 | 240,244 | 13.67 | 2 hrs 45 mins |
5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,068,397 | 233,726 | 13.13 | 2 hrs 50 mins |
6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 2,983,926 | 232,014 | 12.86 | 2 hrs 52 mins |
7 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 2,514,000 | 219,905 | 11.43 | 2 hrs 6 mins |
8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,396,234 | 216,271 | 11.08 | 2 hrs 10 mins |
9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,312,233 | 213,418 | 10.83 | 2 hrs 13 mins |
10 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,199,908 | 210,683 | 10.44 | 2 hrs 18 mins |
11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,107,722 | 207,478 | 10.16 | 2 hrs 22 mins |
12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 1,976,384 | 202,206 | 9.77 | 2 hrs 27 mins |
13 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 1,907,599 | 200,624 | 9.51 | 3 hrs 31 mins |
14 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 1,894,443 | 200,516 | 9.45 | 3 hrs 32 mins |
15 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 1,828,309 | 196,971 | 9.28 | 3 hrs 35 mins |
16 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 1,827,284 | 198,315 | 9.21 | 3 hrs 36 mins |
17 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,710,816 | 194,051 | 8.82 | 3 hrs 43 mins |
18 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,687,501 | 191,885 | 8.79 | 3 hrs 44 mins |
19 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,557,957 | 187,226 | 8.32 | 3 hrs 53 mins |
20 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,499,378 | 183,133 | 8.19 | 3 hrs 56 mins |
21 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,487,818 | 184,110 | 8.08 | 3 hrs 58 mins |
22 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,484,166 | 184,371 | 8.05 | 3 hrs 59 mins |
23 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,360,929 | 177,991 | 7.65 | 3 hrs 8 mins |
24 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 1,261,038 | 175,144 | 7.20 | 3 hrs 20 mins |
25 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,139,402 | 160,253 | 7.11 | 3 hrs 23 mins |
26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,089,775 | 163,697 | 6.66 | 4 hrs 36 mins |
27 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,035,495 | 164,663 | 6.29 | 4 hrs 49 mins |
28 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 974,811 | 160,471 | 6.07 | 4 hrs 57 mins |
29 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 912,480 | 156,709 | 5.82 | 4 hrs 7 mins |
30 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 698,039 | 143,493 | 4.86 | 5 hrs 56 mins |
31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 637,204 | 139,005 | 4.58 | 5 hrs 14 mins |
32 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 599,001 | 135,864 | 4.41 | 5 hrs 27 mins |
33 | GeForce GTX 1650 TU117 [GeForce GTX 1650] 3091 |
Nvidia | TU117 | 529,124 | 131,495 | 4.02 | 6 hrs 58 mins |
34 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 504,873 | 129,151 | 3.91 | 6 hrs 8 mins |
35 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 473,052 | 121,088 | 3.91 | 6 hrs 9 mins |
36 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 445,911 | 124,207 | 3.59 | 7 hrs 41 mins |
37 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 337,389 | 113,213 | 2.98 | 8 hrs 3 mins |
38 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 265,239 | 104,146 | 2.55 | 9 hrs 25 mins |
39 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 258,230 | 103,243 | 2.50 | 10 hrs 36 mins |
40 | GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile] |
Nvidia | GP107M | 233,735 | 99,403 | 2.35 | 10 hrs 12 mins |
41 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 159,193 | 53,545 | 2.97 | 8 hrs 4 mins |
42 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 155,350 | 53,545 | 2.90 | 8 hrs 16 mins |
43 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 122,573 | 80,727 | 1.52 | 16 hrs 48 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
PPDDB data as of Tuesday, 31 January 2023 00:15:00
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|