PROJECT #18488 RESEARCH FOR COVID-19
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

In fragment-based drug discovery, high-throughput screening is used to identify a set of simple, low-affinity (milli-to-micromolar) molecular fragments that bind a protein target.

Information about the chemical and structural properties of the fragments are then used to inform the design of more potent binders.

Are molecular simulations of fragment binding up to the task of providing similarly accurate information? The recent availability of crystallographic fragment screening datasets, along with high-resolution structures of fragment-bound protein receptors, offers a unique opportunity to test this hypothesis.
In this project, we are simulating the binding of several small-molecule fragments to the SARS CoV-2 main protease dimer, the results of which we will compare to crystallographic fragment-screening data generated as part of the COVID Moonshot..

PROJECT INFO

Manager(s): Prof. Vincent Voelz

Institution: Temple University

PROJECT WORK UNIT SUMMARY

Atoms: 131,051

Core: 0x22

Status: Beta

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 20 March 2023 06:14:51

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 8,977,484 663,839 13.52 2 hrs 46 mins
2 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 4,266,466 518,801 8.22 3 hrs 55 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 20 March 2023 06:14:51

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