Research section Research projects and references

Research: CANCER Folding Project #18111

Project #18111 overview

Project Summary AI Beta

Scientists are using computer simulations to study how a protein called KRas interacts with small molecules. They hope this will lead to new drugs that target KRas, which is involved in many cancers. The project is led by Roivant Sciences and data is publicly available online.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Rafal Wiewiora
Institution
Roivant Sciences (Silicon Therapeutics)

Work unit

Atoms
52,212
Core
OPENMM_22
Status
Beta
Source material

Official Project Description

18110: simulation for the complex of KRas(G12D)-GDP-KRpep-2d, with the initial structure of peptide ligand in the known binding position, exploring the binding dynamics of the complex and binding poses. 18111: binding simulation for the complex of KRas(G12D)-GDP-KRpep-2d, with the initial structure of peptide ligand 35A away from the protein center, exploring if the correct binding pose will be observed through the simulation. 18112-18114: binding simulation for the complex of KRas(G12D)-GDP-peptide, the binding pocket and binding pose is unknown.

We start with 100 randomly sampled initial structures, in each structure, the ligand center is 40A away from the protein center, with random direction and rotation.

The simulation is aimed to identify potential binding pocket and binding poses of the peptide.

The interactions between KRas protein and the peptide would provide insights in designing small molecules that bind with KRas. 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/ All data is being made publicly available in real time at https://console.cloud.google.com/storage/browser/stxfah-bucket.

Performance data

Hardware Performance for Project 18111

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 21:53:49

GPU PPD Averages

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 4,963,193 320,359 15.49 1 hrs 33 mins
2 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,651,788 314,014 14.81 1 hrs 37 mins
3 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 2,598,519 258,797 10.04 2 hrs 23 mins
4 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,335,081 251,345 9.29 2 hrs 35 mins
5 Radeon VII
Vega 20 [Radeon VII] 13,284
AMD Vega 20 1,307,531 205,883 6.35 3 hrs 47 mins
6 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 895,262 181,518 4.93 4 hrs 52 mins
7 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 326,897 129,479 2.52 9 hrs 30 mins
8 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 37,822 67,000 0.56 42 hrs 31 mins