Research section Research projects and references

Research: CANCER Folding Project #18120

Project #18120 overview

Project Summary AI Beta

The project relates to studying the KRas protein, which is involved in cell growth and cancer. Scientists are using computer simulations to understand how drugs can block this protein's activity. This research could lead to new treatments for cancers that have a mutated KRas gene.
Automated summary; simplified and may not be fully accurate.

Project team

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

Work unit

Atoms
25,000
Core
OPENMM_22
Status
Beta
Source material

Official Project Description

We are simulating publicly available protein and small molecule structures of the currently very hot cancer target KRas, see https://www.fiercepharma.com/pharma/amgen-s-lumakras-becomes-first-fda-approved-kras-inhibitor-for-lung-cancer-patients for recent developments.

Folding@home has previously looked at this protein (in project 10490), and the following part of the description is copied from there: This project is "studying a small protein called KRAS, which forms a key link in growth signaling and cancer.

This gene is something like a molecular switch with a timer.

When it is bound to a molecule called GDP, it is off, and does not signal that the cell should grow.

However, other proteins can cause it to swap its GDP for a GTP, turning KRAS on.

In the on state, it signals that the cell should grow and divide.

Normally, after some time, KRAS, with the aid of some partners, will chemically convert its GTP to GDP and return to its inactive state. In many cancers, this protein becomes mutated, and cannot return to its off state.

The result? The cells continue to divide without limit.

What’s worse, cancers with this protein mutated tend to have much poorer prognoses.

As a result, scientists have been trying to target this protein for decades." We are investigating the dynamic behavior of KRas with these publicly disclosed inhibitors so that we can apply this knowledge to our own drug design.

At the same time, we are further testing the adaptive sampling methodology.

All data is being made publicly available at https://console.cloud.google.com/storage/browser/stxfah-bucket, and insights from methodology developments will be shared.

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 as soon as it is received at https://console.cloud.google.com/storage/browser/stxfah-bucket.

Performance data

Hardware Performance for Project 18120

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

Data as of Sunday, 02 August 2026 21:53:40

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 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,665,823 104,427 15.95 1 hrs 30 mins
2 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 630,107 81,456 7.74 3 hrs 6 mins
3 GeForce 920M
GK208 [GeForce 920M]
Nvidia GK208 43,629 33,521 1.30 18 hrs 26 mins
4 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 39,705 31,058 1.28 18 hrs 46 mins