PROJECT #16498 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 Sunday, 04 December 2022 18:13:27

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 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,729,096 274,764 17.21 1 hrs 24 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,079,866 257,315 15.86 2 hrs 31 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 3,613,256 248,560 14.54 2 hrs 39 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 3,501,391 248,601 14.08 2 hrs 42 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 3,349,366 241,206 13.89 2 hrs 44 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,253,980 237,306 13.71 2 hrs 45 mins
7 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 3,228,786 229,930 14.04 2 hrs 43 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,014,736 235,819 12.78 2 hrs 53 mins
9 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,971,893 234,658 12.66 2 hrs 54 mins
10 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 2,923,628 233,050 12.55 2 hrs 55 mins
11 GeForce RTX 3070 Mobile / Max-Q
GA104M [GeForce RTX 3070 Mobile / Max-Q]
Nvidia GA104M 2,830,537 232,191 12.19 2 hrs 58 mins
12 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,671,051 231,093 11.56 2 hrs 5 mins
13 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,500,671 220,472 11.34 2 hrs 7 mins
14 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,342,593 211,826 11.06 2 hrs 10 mins
15 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,307,764 215,625 10.70 2 hrs 15 mins
16 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,899,395 202,471 9.38 3 hrs 34 mins
17 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 1,853,935 200,131 9.26 3 hrs 35 mins
18 GeForce RTX 3060
GA104 [GeForce RTX 3060]
Nvidia GA104 1,816,769 199,714 9.10 3 hrs 38 mins
19 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,741,861 178,124 9.78 2 hrs 27 mins
20 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,587,614 169,492 9.37 3 hrs 34 mins
21 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 1,573,981 191,164 8.23 3 hrs 55 mins
22 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,540,916 188,881 8.16 3 hrs 57 mins
23 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,415,131 182,356 7.76 3 hrs 6 mins
24 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,244,146 176,457 7.05 3 hrs 24 mins
25 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 1,237,715 175,350 7.06 3 hrs 24 mins
26 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,193,674 173,096 6.90 3 hrs 29 mins
27 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,115,465 167,600 6.66 4 hrs 36 mins
28 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,071,472 167,350 6.40 4 hrs 45 mins
29 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,050,380 164,932 6.37 4 hrs 46 mins
30 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 962,682 161,280 5.97 4 hrs 1 mins
31 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 770,324 151,171 5.10 5 hrs 43 mins
32 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 582,269 139,991 4.16 6 hrs 46 mins
33 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 578,019 135,556 4.26 6 hrs 38 mins
34 P104-100
GP104 [P104-100]
Nvidia GP104 542,442 133,937 4.05 6 hrs 56 mins
35 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 527,214 132,253 3.99 6 hrs 1 mins
36 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 473,169 127,645 3.71 6 hrs 28 mins
37 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 170,629 90,893 1.88 13 hrs 47 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 04 December 2022 18:13:27

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