Predicting drug absorption rates through human skin

Yi Sun; Lun Tak Lam; Gary Moss; Maria Prapopoulou; Rod Adams; Neil Davey; David Gray; Mark Brown

doi:10.1109/IJCNN.2010.5596603

Predicting drug absorption rates through human skin

Yi Sun, Lun Tak Lam, Gary Moss, Maria Prapopoulou, Rod Adams, Neil Davey, David Gray, Mark Brown

School of Life and Health Sciences

University of Hertfordshire

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.

Original language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs	https://doi.org/10.1109/IJCNN.2010.5596603
Publication status	Published - 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010

Other

Other	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Country/Territory	Spain
City	Barcelona
Period	18/07/10 → 23/07/10

Access to Document

10.1109/IJCNN.2010.5596603

Cite this

@inproceedings{1c0fce4ea39c4a4c987bba29556b6c2d,

title = "Predicting drug absorption rates through human skin",

abstract = "Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.",

author = "Yi Sun and Lam, {Lun Tak} and Gary Moss and Maria Prapopoulou and Rod Adams and Neil Davey and David Gray and Mark Brown",

year = "2010",

doi = "10.1109/IJCNN.2010.5596603",

language = "English",

isbn = "9781424469178",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

}

Sun, Y, Lam, LT, Moss, G, Prapopoulou, M, Adams, R, Davey, N, Gray, D & Brown, M 2010, Predicting drug absorption rates through human skin. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010., 5596603, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, 18/07/10. https://doi.org/10.1109/IJCNN.2010.5596603

TY - GEN

T1 - Predicting drug absorption rates through human skin

AU - Sun, Yi

AU - Lam, Lun Tak

AU - Moss, Gary

AU - Prapopoulou, Maria

AU - Adams, Rod

AU - Davey, Neil

AU - Gray, David

AU - Brown, Mark

PY - 2010

Y1 - 2010

N2 - Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.

AB - Predicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.

UR - http://www.scopus.com/inward/record.url?scp=79959420620&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2010.5596603

DO - 10.1109/IJCNN.2010.5596603

M3 - Conference contribution

AN - SCOPUS:79959420620

SN - 9781424469178

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

Y2 - 18 July 2010 through 23 July 2010

ER -

Predicting drug absorption rates through human skin

Abstract

Other

Access to Document

Other files and links

Fingerprint

Cite this