RESEARCH: CANCER
FOLDING PROJECT #16498 PROFILE
PROJECT TEAM
Manager(s): Sukrit SinghInstitution: Memorial Sloan-Kettering Cancer-Center
Project URL: View Project Website
WORK UNIT INFO
Atoms: 59,897Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project uses computer simulations to find new shapes of a protein called MET kinase that's involved in some cancers. The goal is to discover these new shapes so scientists can design better cancer drugs that work even when the cancer becomes resistant to existing treatments like crizotinib.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
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!.
RELATED TERMS GLOSSARY AI BETA
drug discovery
The process of identifying and developing new medications.
Drug discovery is a complex process that involves identifying promising drug candidates, testing their effectiveness and safety, and ultimately bringing them to market. This often involves a multi-disciplinary approach, combining expertise in chemistry, biology, pharmacology, and clinical research.
cancer
A group of diseases characterized by uncontrolled cell growth.
Cancer is a broad term for a group of diseases that involve the abnormal and uncontrolled growth of cells. This can lead to the formation of tumors, which can invade surrounding tissues and spread to other parts of the body.
protein
A large biomolecule composed of amino acids.
Proteins are essential building blocks of life, playing a vast array of roles within cells and organisms. They are responsible for transporting molecules, catalyzing reactions, providing structural support, and much more.
drug design
The process of designing and developing new drugs.
Drug design is a multi-faceted process that involves identifying potential drug targets, understanding their structure and function, and then designing molecules that can interact with them to produce a desired therapeutic effect.
inhibitor
A substance that blocks or reduces the activity of a specific enzyme or protein.
Inhibitors are molecules that can bind to enzymes or proteins and prevent them from carrying out their normal functions. This is often used in drug development to target specific proteins involved in disease pathways.
MET kinase
Methionine Aminopeptidase 2 Kinase
MET kinase is a protein involved in cell growth and signaling. It plays a role in several types of cancer, making it a target for drug development.
crizotinib
A drug that inhibits MET kinase.
Crizotinib is a type of targeted therapy used to treat certain types of lung cancer. It works by blocking the activity of the MET kinase protein, which is involved in the growth and spread of cancer cells.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Saturday, 25 April 2026 21:44:59|
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 | 1 hrs 31 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,613,256 | 248,560 | 14.54 | 1 hrs 39 mins |
| 4 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 3,501,391 | 248,601 | 14.08 | 1 hrs 42 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 3,349,366 | 241,206 | 13.89 | 1 hrs 44 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,253,980 | 237,306 | 13.71 | 1 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 | 1 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 | 1 hrs 53 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,971,893 | 234,658 | 12.66 | 1 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 | 1 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 | 1 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 | 2 hrs 34 mins |
| 17 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 1,853,935 | 200,131 | 9.26 | 2 hrs 35 mins |
| 18 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,816,769 | 199,714 | 9.10 | 2 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 | 2 hrs 34 mins |
| 21 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 1,573,981 | 191,164 | 8.23 | 2 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 | 2 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 | 3 hrs 36 mins |
| 28 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,071,472 | 167,350 | 6.40 | 3 hrs 45 mins |
| 29 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,050,380 | 164,932 | 6.37 | 3 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 | 4 hrs 43 mins |
| 32 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 582,269 | 139,991 | 4.16 | 5 hrs 46 mins |
| 33 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 578,019 | 135,556 | 4.26 | 5 hrs 38 mins |
| 34 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 542,442 | 133,937 | 4.05 | 5 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 | 12 hrs 47 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Saturday, 25 April 2026 21:44:59|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|