RESEARCH: CANCER
FOLDING PROJECT #12465 PROFILE

PROJECT TEAM

Manager(s): Prof. Vincent Voelz
Institution: Temple University

WORK UNIT INFO

Atoms: 17,300
Core: 0xa8
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

This project explores using computer simulations to predict how well drugs dissolve in water versus fat. This 'logP' value is important because it affects how well a drug works in the body. Researchers are testing different simulation methods and seeing if different computer models impact the accuracy of these predictions.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

A key challenge in computational drug discovery is developing and testing methods to predict experimental partition coefficients for the relative solubility of organic molecules in aqueous vs.

non-polar media.

The logarithm of the partition coefficient, logP, is an important predictor of lipophilicity, which dictates the bioavailability of drugs.

This CPU project is testing expanded-ensemble (EE) simulations as a method for the calculation of logP and seeing if forcefield selection effects these calculations.

RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

partition coefficient

Measure of a substance's solubility in two immiscible solvents.

Scientific: Biotechnology
Pharmacology / Drug Discovery

The partition coefficient describes how well a substance dissolves in oil compared to water. It's crucial for understanding how drugs will move through the body because it affects their absorption and distribution.


logP

Logarithm of the partition coefficient

Scientific: Biotechnology
Pharmacology / Drug Discovery

logP is a simplified way to represent the partition coefficient. A higher logP value means a substance is more soluble in oil and less soluble in water.


lipophilicity

The tendency of a substance to dissolve in lipids or fats.

Scientific: Biotechnology
Pharmacology / Drug Discovery

Lipophilicity describes how well a substance mixes with fats. Drugs need the right balance of lipophilicity to be absorbed into the bloodstream and reach their target tissues.


bioavailability

The proportion of a drug that reaches the bloodstream.

Scientific: Biotechnology
Pharmacology / Drug Discovery

Bioavailability measures how much of a drug actually makes it into the body's circulation. Factors like lipophilicity influence bioavailability.


forcefield

A set of parameters used in molecular simulations.

Scientific: Biotechnology
Computational Chemistry / Drug Discovery

Forcefields are like rules that govern how atoms interact with each other in computer simulations. They help researchers predict the behavior of molecules.


simulations

Computer models of chemical processes.

Scientific: Biotechnology
Computational Chemistry / Drug Discovery

Simulations allow researchers to study molecules and their interactions without needing physical experiments. They can be used to predict properties like solubility.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:19
Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:34:19
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN THREADRIPPER 7980X 64-CORES 64 57,644 3,689,216 AMD
2 CORE ULTRA 7 265K 20 78,243 1,564,860 Intel
3 RYZEN 9 9950X 16-CORE 32 36,924 1,181,568 AMD
4 RYZEN 7 7700X 8-CORE 16 68,638 1,098,208 AMD
5 RYZEN 9 7900X 12-CORE 24 33,240 797,760 AMD
6 RYZEN 7 5800X3D 8-CORE 16 47,112 753,792 AMD
7 RYZEN 9 7950X3D 16-CORE 32 21,539 689,248 AMD
8 RYZEN 7 7700 8-CORE 16 38,658 618,528 AMD
9 RYZEN 9 7900 12-CORE 24 25,267 606,408 AMD
10 RYZEN 7 7800X3D 8-CORE 16 37,655 602,480 AMD
11 RYZEN 9 5950X 16-CORE 32 16,156 516,992 AMD
12 RYZEN 9 3950X 16-CORE 32 15,720 503,040 AMD
13 12TH GEN CORE I5-12400F 12 39,705 476,460 Intel
14 12TH GEN CORE I7-12700F 20 21,657 433,140 Intel
15 RYZEN 7 5700X3D 8-CORE 16 26,356 421,696 AMD
16 RYZEN 9 3900X 12-CORE 24 17,114 410,736 AMD
17 13TH GEN CORE I5-13600K 14 27,780 388,920 Intel
18 RYZEN 7 5700G 16 20,840 333,440 AMD
19 RYZEN 7 5700X 8-CORE 16 19,386 310,176 AMD
20 RYZEN 5 5600G 12 22,902 274,824 AMD
21 CORE I7-10700K CPU @ 3.80GHZ 16 16,179 258,864 Intel
22 RYZEN 5 5600 6-CORE 12 17,019 204,228 AMD
23 RYZEN 7 3700X 8-CORE 16 12,658 202,528 AMD
24 11TH GEN CORE I7-11800H @ 2.30GHZ 16 11,799 188,784 Intel
25 RYZEN 7 5800H 16 11,701 187,216 AMD
26 RYZEN 5 5600X 6-CORE 12 13,478 161,736 AMD
27 CORE I7-5820K CPU @ 3.30GHZ 12 10,460 125,520 Intel
28 XEON CPU E5-2630 V4 @ 2.20GHZ 40 2,991 119,640 Intel
29 CORE I7-8700 CPU @ 3.20GHZ 12 9,791 117,492 Intel
30 11TH GEN CORE I7-11700F @ 2.50GHZ 16 6,661 106,576 Intel
31 CORE I5-14500 20 5,064 101,280 Intel
32 RYZEN 5 5500U 12 8,219 98,628 AMD
33 CORE I5-10400 CPU @ 2.90GHZ 12 6,955 83,460 Intel
34 RYZEN 5 3400G 8 9,961 79,688 AMD
35 11TH GEN CORE I5-11400 @ 2.60GHZ 12 5,427 65,124 Intel
36 RYZEN 9 5900X 12-CORE 24 AMD
37 RYZEN 7 5800X 8-CORE 16 AMD
38 RYZEN 5 3600X 6-CORE 12 AMD
39 RYZEN 5 7600 6-CORE 12 AMD
40 CORE I7-14700K 28 Intel
41 XEON CPU E5-2680 V2 @ 2.80GHZ 40 Intel