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Research: CANCER Folding Project #17802

Project #17802 overview

Project Summary AI Beta

This project explores using AI to understand how proteins work, specifically focusing on BCL-XL, a protein linked to lymphoma. Researchers are testing new methods called 'adaptive sampling' to build models of proteins from different data points. One version starts with many protein structures and connects them, while another starts with just one and predicts the rest.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Rafal Wiewiora
Institution
Memorial Sloan Kettering Cancer Center

Work unit

Atoms
22,500
Core
OPENMM_22
Status
Public

No related projects listed.

Source material

Official Project Description

BCL-XL apoptotic protein --- a drug target in lymphoma.

More info coming soon.

Together with project 17800 this is also testing new methodology --- adaptive sampling.

This is the adaptive (first time on F@h) sampling version of the system. Project 17801 tests a 'simpler' version of the problem for the adaptive sampling algorithm --- we start from 35 crystal structures and attempt to connect them into one model. Project 17802 tests a 'harder' version of the problem --- we start from just 1 crystal structure and attempt to predict the other ones.

Performance data

Hardware Performance for Project 17802

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

Data as of Sunday, 02 August 2026 21:55:36

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 Super
TU106 [GeForce RTX 2060 SUPER]		 Nvidia TU106 1,897,763 115,133 16.48 1 hrs 27 mins
2 GeForce RTX 2060
TU106 [Geforce RTX 2060]		 Nvidia TU106 1,778,539 111,243 15.99 1 hrs 30 mins
3 GeForce RTX 2060
TU104 [GeForce RTX 2060]		 Nvidia TU104 1,697,619 110,849 15.31 1 hrs 34 mins
4 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]		 Nvidia TU106M 1,558,931 107,902 14.45 1 hrs 40 mins
5 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463		 Nvidia GP104 1,348,497 102,753 13.12 1 hrs 50 mins
6 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632		 Nvidia GM200 958,773 91,656 10.46 2 hrs 18 mins
7 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]		 Nvidia TU116 863,937 88,044 9.81 2 hrs 27 mins
8 GeForce GTX 980
GM204 [GeForce GTX 980] 4612		 Nvidia GM204 709,933 83,057 8.55 2 hrs 48 mins
9 Quadro RTX 4000
TU104GL [Quadro RTX 4000]		 Nvidia TU104GL 693,376 69,987 9.91 2 hrs 25 mins
10 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372		 Nvidia GP106 681,419 81,825 8.33 2 hrs 53 mins
11 GeForce GTX 970
GM204 [GeForce GTX 970] 3494		 Nvidia GM204 657,260 80,938 8.12 2 hrs 57 mins
12 GeForce GTX 1650
TU116 [GeForce GTX 1650] 2984		 Nvidia TU116 591,798 77,957 7.59 3 hrs 10 mins
13 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]		 Nvidia TU117M 583,328 78,589 7.42 3 hrs 14 mins
14 P106-090
GP106 [P106-090]		 Nvidia GP106 296,308 61,968 4.78 5 hrs 1 mins
15 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]		 Nvidia TU116 289,008 64,558 4.48 5 hrs 22 mins
16 P104-100
GP104 [P104-100]		 Nvidia GP104 266,257 59,954 4.44 5 hrs 24 mins
17 GeForce GTX 660 Ti
GK104 [GeForce GTX 660 Ti] 2634		 Nvidia GK104 234,146 57,403 4.08 5 hrs 53 mins
18 P106-100
GP106 [P106-100]		 Nvidia GP106 203,822 54,787 3.72 6 hrs 27 mins
19 Quadro K2200
GM107GL [Quadro K2200]		 Nvidia GM107GL 157,030 50,265 3.12 7 hrs 41 mins
20 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389		 Nvidia GM107 148,361 49,142 3.02 7 hrs 57 mins
21 GeForce GT 1030
GP108 [GeForce GT 1030] 1127		 Nvidia GP108 129,825 47,053 2.76 8 hrs 42 mins