RESEARCH: CANCER
FOLDING PROJECT #12464 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 7,600
Core: 0xa8
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

This project is figuring out if computer simulations can predict how well drugs dissolve in the body. It's testing different simulation methods and seeing if the choice of software affects the results. This helps scientists design better medicines!

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

A measure of a substance's solubility in two immiscible solvents.

Technical: Biotechnology
Pharmacology / Drug Discovery

The partition coefficient describes how easily a substance dissolves in both water and oil. It's essential for understanding drug absorption and distribution within the body. A higher partition coefficient indicates greater solubility in oil, which can influence a drug's ability to cross cell membranes.


logP

Logarithm of the partition coefficient

Acronym: Biotechnology
Pharmacology / Drug Discovery

logP is a simplified way to express the partition coefficient. It's often used in drug development to predict how well a molecule will dissolve in fat versus water. Higher logP values generally indicate better absorption into tissues.


lipophilicity

The tendency of a substance to dissolve in lipids (fats).

Scientific: Biotechnology
Pharmacology / Drug Discovery

Lipophilicity describes how much a molecule prefers to dissolve in fats compared to water. It's crucial for drug effectiveness because many drugs need to pass through lipid membranes to reach their targets within the body.


bioavailability

The proportion of a drug that reaches the systemic circulation.

Technical: Biotechnology
Pharmacology / Drug Discovery

Bioavailability refers to how much of a drug actually enters the bloodstream after administration. Factors like solubility and metabolism can influence bioavailability.


forcefield

A set of parameters used in computer simulations to model the interactions between atoms in molecules.

Technical: Biotechnology
Computational Biology / Molecular Dynamics Simulations

Forcefields are like mathematical rules that govern how atoms behave in simulations. They're essential for accurately predicting the movement and interactions of molecules.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:34:20
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 06:34:20
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 CORE ULTRA 7 265K 20 59,772 1,195,440 Intel
2 RYZEN 9 9950X 16-CORE 32 35,963 1,150,816 AMD
3 RYZEN 9 7950X3D 16-CORE 32 34,691 1,110,112 AMD
4 APPLE M4 PRO 14 75,283 1,053,962 Apple
5 12TH GEN CORE I7-12700K 20 47,009 940,180 Intel
6 RYZEN 7 7700 8-CORE 16 49,925 798,800 AMD
7 RYZEN 7 7800X3D 8-CORE 16 49,302 788,832 AMD
8 RYZEN 9 7900 12-CORE 24 28,911 693,864 AMD
9 RYZEN 9 7900X 12-CORE 24 28,638 687,312 AMD
10 RYZEN 9 5950X 16-CORE 32 16,610 531,520 AMD
11 12TH GEN CORE I7-12700F 20 22,799 455,980 Intel
12 RYZEN 7 5700X 8-CORE 16 28,272 452,352 AMD
13 RYZEN 7 5700X3D 8-CORE 16 27,709 443,344 AMD
14 RYZEN 9 3950X 16-CORE 32 12,405 396,960 AMD
15 RYZEN 5 5600X 6-CORE 12 29,853 358,236 AMD
16 RYZEN 5 5600G 12 26,629 319,548 AMD
17 13TH GEN CORE I5-13600K 14 21,898 306,572 Intel
18 RYZEN 5 5600 6-CORE 12 25,383 304,596 AMD
19 RYZEN 7 5700G 16 15,730 251,680 AMD
20 RYZEN 9 3900X 12-CORE 24 9,476 227,424 AMD
21 RYZEN 7 8845HS W/ RADEON(TM) 780M GRAPHICS 16 13,470 215,520 AMD
22 XEON CPU E5-2680 V2 @ 2.80GHZ 40 5,273 210,920 Intel
23 RYZEN THREADRIPPER 2950X 16-CORE 32 5,929 189,728 AMD
24 11TH GEN CORE I7-11800H @ 2.30GHZ 16 10,194 163,104 Intel
25 RYZEN 7 5800H 16 10,052 160,832 AMD
26 RYZEN 7 3700X 8-CORE 16 8,855 141,680 AMD
27 APPLE M3 8 17,335 138,680 Apple
28 RYZEN 9 3900XT 12-CORE 24 5,761 138,264 AMD
29 APPLE M1 8 15,304 122,432 Apple
30 CORE I7-5820K CPU @ 3.30GHZ 12 10,117 121,404 Intel
31 CORE I5-14500 20 5,959 119,180 Intel
32 12TH GEN CORE I9-12900K 24 4,958 118,992 Intel
33 CORE I7-8700 CPU @ 3.20GHZ 12 9,670 116,040 Intel
34 CORE I5-9400 CPU @ 2.90GHZ 6 18,154 108,924 Intel
35 11TH GEN CORE I7-11700F @ 2.50GHZ 16 5,566 89,056 Intel
36 APPLE M2 PRO 10 7,417 74,170 Apple
37 CORE I5-10400 CPU @ 2.90GHZ 12 6,159 73,908 Intel
38 RYZEN 5 3400G 8 8,183 65,464 AMD
39 XEON CPU X5660 @ 2.80GHZ 24 2,410 57,840 Intel
40 CORE I7-10750H CPU @ 2.60GHZ 12 3,584 43,008 Intel
41 RYZEN 9 5900X 12-CORE 24 AMD
42 RYZEN 5 3600X 6-CORE 12 AMD
43 RYZEN 7 7700X 8-CORE 16 AMD
44 RYZEN 5 7600 6-CORE 12 AMD
45 RYZEN 7 9800X3D 8-CORE 16 AMD
46 RYZEN THREADRIPPER PRO 7955WX 16-CORES 32 AMD