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- W2277881998 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).KeywordsArtificial Neural NetworkPhotodynamic TherapyVariable Selection MethodArtificial Neural Network ArchitecturePhotosensitizer ActivityThese 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." @default.
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- W2277881998 date "2000-01-01" @default.
- W2277881998 modified "2023-10-06" @default.
- W2277881998 title "Prediction of Photosensitizers Activity in Photodynamic Therapy Using Artificial Neural Networks: A 3D—QSAR Study" @default.
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- W2277881998 doi "https://doi.org/10.1007/978-1-4471-0513-8_45" @default.
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