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 |
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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 |
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