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Prediction of Photosensitizers Activity in Photodynamic Therapy Using Artificial Neural Networks: A 3D—QSAR Study

  • Rozália Vanyúr
  • Károly Héberger
  • István Kövesdi
  • Judit Jakus
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

Biological activity in photodynamic therapy was predicted from the molecular structure of pyropheophorbide derivatives using artificial neural networks (ANN). First, the structure of molecules was optimized and various descriptors were calculated. ANN architecture was optimized while suitable descriptors were selected applying a novel variable selection method.

The reliability of models was tested by cross-validation and randomization of biological activity data. Models are able to predict biological activity from the molecular structure of the phorbide derivatives with a leave-one-out crossvalidation Q2 of 0.956. The size of the substituents is decisive in the third direction (perpendicular to the main plain of the molecules).

Keywords

Artificial Neural Network Photodynamic Therapy Variable Selection Method Artificial Neural Network Architecture Photosensitizer Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Rozália Vanyúr
    • 1
  • Károly Héberger
    • 1
  • István Kövesdi
    • 2
  • Judit Jakus
    • 1
  1. 1.Institute of Chemistry, Chemical Research CenterHungarian Academy of SciencesBudapestHungary
  2. 2.EGIS Pharmaceutical Company Ltd.BudapestHungary

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