PROJECT #16499 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE
PROJECT SUMMARY
In drug discovery, particularly that of cancer, maximizing state exploration is a useful novel strategy – providing new protein states and conformations to point drug design methods at increases the likelihood that a potential binder and inhibitor may be found.
However, in many cases a new state that is "useful for design" (ie.
ones distinct enough to be worth targeting to identify novel drugs) require a lot of sampling or simulation.
Sometimes, even exascale computers like Folding@home are not enough! Adaptive methods are very powerful here, but have the drawback of requiring system knowledge, or having to guess which protein features are worth adaptively exploring on, which may not always turn out to be true.
Another promising strategy, explored in these projects, is to "Accelerate" the simulations.
By broadly applying "boosters" to the simulation, we effectively "flatten" the energy landscape of a protein's conformations, allowing the protein to visit states more easily than it normally would.
Alongside the ability to discover new states that we can seed simulations of, just like adaptive sampling simulations, these boosters have specific technical and physiclal properties that allow us to infer something about a new state's "accessibility" (ie where it exists on the landscape). In projects 16497–16499 we test three such boosters to accelerate our simulations to identify how well boosted simulations work for our purposes.
Here we apply it to the protein MET kinase, a protein drug target in many cancers such as non-small-cell lung carcinoma.
MET kinase is targeted by the drug crizotinib but often evolves resistance against the drug, rendering it ineffective.
With our boosted simulations we hope to observe never before seen states of MET!.
PROJECT INFO
Manager(s): Sukrit Singh
Institution: Memorial Sloan-Kettering Cancer-Center
Project URL: http://sukritsingh.github.io/
PROJECT WORK UNIT SUMMARY
Atoms: 59,897
Core: OPENMM_22
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Monday, 27 June 2022 11:45:25
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 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 4,596,323 | 373,491 | 12.31 | 2 hrs 57 mins |
2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,596,158 | 374,745 | 12.26 | 2 hrs 57 mins |
3 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,413,920 | 371,857 | 11.87 | 2 hrs 1 mins |
4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,317,469 | 355,207 | 12.15 | 2 hrs 58 mins |
5 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,040,719 | 352,525 | 11.46 | 2 hrs 6 mins |
6 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 4,006,694 | 361,061 | 11.10 | 2 hrs 10 mins |
7 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,500,813 | 342,886 | 10.21 | 2 hrs 21 mins |
8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,499,245 | 341,522 | 10.25 | 2 hrs 21 mins |
9 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,263,454 | 328,782 | 9.93 | 2 hrs 25 mins |
10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,132,518 | 329,445 | 9.51 | 3 hrs 31 mins |
11 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 2,887,406 | 323,910 | 8.91 | 3 hrs 42 mins |
12 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,694,843 | 318,717 | 8.46 | 3 hrs 50 mins |
13 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,621,784 | 313,207 | 8.37 | 3 hrs 52 mins |
14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,536,057 | 308,704 | 8.22 | 3 hrs 55 mins |
15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,532,483 | 309,545 | 8.18 | 3 hrs 56 mins |
16 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,378,844 | 299,695 | 7.94 | 3 hrs 1 mins |
17 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 2,128,195 | 294,955 | 7.22 | 3 hrs 20 mins |
18 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,785,117 | 275,421 | 6.48 | 4 hrs 42 mins |
19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,784,270 | 272,338 | 6.55 | 4 hrs 40 mins |
20 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,683,213 | 272,792 | 6.17 | 4 hrs 53 mins |
21 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,446,034 | 257,347 | 5.62 | 4 hrs 16 mins |
22 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,425,646 | 251,883 | 5.66 | 4 hrs 14 mins |
23 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,308,859 | 253,231 | 5.17 | 5 hrs 39 mins |
24 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,291,492 | 246,931 | 5.23 | 5 hrs 35 mins |
25 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,177,543 | 240,051 | 4.91 | 5 hrs 54 mins |
26 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,146,022 | 243,319 | 4.71 | 5 hrs 6 mins |
27 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,082,075 | 220,202 | 4.91 | 5 hrs 53 mins |
28 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 943,379 | 223,194 | 4.23 | 6 hrs 41 mins |
29 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 902,239 | 224,899 | 4.01 | 6 hrs 59 mins |
30 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 868,796 | 217,702 | 3.99 | 6 hrs 1 mins |
31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 860,948 | 217,146 | 3.96 | 6 hrs 3 mins |
32 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 614,441 | 193,613 | 3.17 | 8 hrs 34 mins |
33 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 567,622 | 197,677 | 2.87 | 8 hrs 21 mins |
34 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 543,245 | 185,007 | 2.94 | 8 hrs 10 mins |
35 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 449,994 | 174,030 | 2.59 | 9 hrs 17 mins |
36 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 193,126 | 130,001 | 1.49 | 16 hrs 9 mins |
37 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 142,525 | 118,705 | 1.20 | 20 hrs 59 mins |
38 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 102,465 | 115,854 | 0.88 | 27 hrs 8 mins |
39 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 85,275 | 99,656 | 0.86 | 28 hrs 3 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
PPDDB data as of Monday, 27 June 2022 11:45:25
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|
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