Fotoszenzibilizátorok fotodinamikus aktivitásának és felhalmozódásának elorejelzése mennyiségi szerkezet-hatás összefüggések segítségével

Translated title of the contribution: Prediction of photodynamic activity of photosensitizers using quantitative structureactivity relationship analysis

Rozália Vanyúr, K. Heberger, J. Jakus, István Kövesdi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The biological activities of a congeneric series of pyropheophorbides used as sensitizers in photodynamic therapy have been predicted using multiple linear regression (MLR) and artificial neural network (ANN) computations. Theoretical descriptors (81) were calculated by 3DNET program based on the three-dimensional structure of the molecules calculated by MM+ method of Hyperchem program package. These input descriptors were tested as independent variables and used for the model building. The predictive abilities of the best fitting models were checked by leave-one-out cross-validation. ANN was suitable to build models for both linear and non-linear relationships. Lipophilicity was enough to predict accumulation of the sensitizers in the target tissue. Direction dependent eigenvalues like atomic position or mass descriptors, which are perpendicular to the main plane of the molecules, were also needed to predict photodynamic activity. Our models were able to predict biological activities of a series of pyropheophorbide derivatives solely on the basis of their three dimensional molecular structures. ANN turned out to be a valuable method for future model building to predict different biological activities of a wide range of porphyrin based molecules.

Original languageHungarian
Pages (from-to)167-172
Number of pages6
JournalMagyar Kemiai Folyoirat, Kemiai Kozlemenyek
Volume107
Issue number4
Publication statusPublished - 2001

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Photosensitizing Agents
Bioactivity
Neural networks
Molecules
Photodynamic therapy
Porphyrins
Linear regression
Molecular structure
Tissue
Derivatives

ASJC Scopus subject areas

  • Chemistry(all)

Cite this

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title = "Fotoszenzibiliz{\'a}torok fotodinamikus aktivit{\'a}s{\'a}nak {\'e}s felhalmoz{\'o}d{\'a}s{\'a}nak elorejelz{\'e}se mennyis{\'e}gi szerkezet-hat{\'a}s {\"o}sszef{\"u}gg{\'e}sek seg{\'i}ts{\'e}g{\'e}vel",
abstract = "The biological activities of a congeneric series of pyropheophorbides used as sensitizers in photodynamic therapy have been predicted using multiple linear regression (MLR) and artificial neural network (ANN) computations. Theoretical descriptors (81) were calculated by 3DNET program based on the three-dimensional structure of the molecules calculated by MM+ method of Hyperchem program package. These input descriptors were tested as independent variables and used for the model building. The predictive abilities of the best fitting models were checked by leave-one-out cross-validation. ANN was suitable to build models for both linear and non-linear relationships. Lipophilicity was enough to predict accumulation of the sensitizers in the target tissue. Direction dependent eigenvalues like atomic position or mass descriptors, which are perpendicular to the main plane of the molecules, were also needed to predict photodynamic activity. Our models were able to predict biological activities of a series of pyropheophorbide derivatives solely on the basis of their three dimensional molecular structures. ANN turned out to be a valuable method for future model building to predict different biological activities of a wide range of porphyrin based molecules.",
author = "Roz{\'a}lia Vany{\'u}r and K. Heberger and J. Jakus and Istv{\'a}n K{\"o}vesdi",
year = "2001",
language = "Hungarian",
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pages = "167--172",
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AU - Jakus, J.

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AB - The biological activities of a congeneric series of pyropheophorbides used as sensitizers in photodynamic therapy have been predicted using multiple linear regression (MLR) and artificial neural network (ANN) computations. Theoretical descriptors (81) were calculated by 3DNET program based on the three-dimensional structure of the molecules calculated by MM+ method of Hyperchem program package. These input descriptors were tested as independent variables and used for the model building. The predictive abilities of the best fitting models were checked by leave-one-out cross-validation. ANN was suitable to build models for both linear and non-linear relationships. Lipophilicity was enough to predict accumulation of the sensitizers in the target tissue. Direction dependent eigenvalues like atomic position or mass descriptors, which are perpendicular to the main plane of the molecules, were also needed to predict photodynamic activity. Our models were able to predict biological activities of a series of pyropheophorbide derivatives solely on the basis of their three dimensional molecular structures. ANN turned out to be a valuable method for future model building to predict different biological activities of a wide range of porphyrin based molecules.

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