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Anti-HIV-1 activities of 20 tetrapyrroles (hematoporphyrin derivatives, meso-tetraphenylporphyrins, a chlorin, and a phthalocyanine) were predicted based on their molecular structures using artificial neural networks. The molecular structures were optimized by HyperChem program using MM+ molecular mechanics and conformational search for the global minimum conformer. Eighty-seven theoretical descriptors were calculated for characterization of molecular structures. The network architecture was optimized, and suitable descriptors were selected applying a novel variable selection method. The 3DNET program was used for the calculation of descriptors and for neural network computations. The reliability of models was tested by randomization of biological activity data, leave-one-out, leave-n-out cross-validation, and external validation process. The predictive ability of the artificial neural network was compared to other model building methods, like multiple linear regressions and partial least squares projection to latent structures. For prediction of anti-HIV-1 activity, the artificial neural network gave the best results at cross-validation processes and at external validation as well. We built four nonlinear models with good predictive ability in all validation steps, which can be applied to predict the anti-HIV-1 activity of tetrapyrrole-type compounds in a much better way than with any other three-dimensional quantitative structure-activity relationship methods published to date.

The biological activities of a congeneric series of pyropheophorbides used as sensitizers in photodynamic therapy have been predicted on the basis of their molecular structures, using multiple linear regression and artificial neural network (ANN) computations. Theoretical descriptors (a total of 81) were calculated by the 3DNET program based on the three-dimensional structure (3D) of the geometry-optimized molecules. These input descriptors were tested as independent variables and used for model building. Systematic descriptor selections yielded models with one, two or three descriptors with good cross-validation results. The predictive abilities of the best fitting models were checked by shuffling and cross-validation procedures. ANN was suitable for building models for both linear and nonlinear relationships. Lipophilicity was sufficient to predict the accumulation of the sensitizers in the target tissue. Weighted holistic invariant molecular descriptors weighted by atomic mass, Van der Waals volume or electronegativity were also needed to predict photodynamic activity properly. Our models were able to predict the biological activities of 13 pyropheophorbide derivatives solely on the basis of their 3D molecular structures. Moreover, linear and nonlinear variable selection methods were compared in models built linearly and nonlinearly. It is expedient to use the same method (linear or nonlinear) for variable selection as for parameter estimation.

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Earlier results studying the effect of excited triplet photosensitizer on the zymosan-stimulated and luminol-dependent chemiluminescence of macrophages have been quantitatively re-evaluated and rate constant data indicate that the effect is due to triplet-doublet interactions between sensitizer and free radicals generated.Such interactions, named Type III mechanism, compete with Type I and Type II mechanisms depending on the experimental environment. This suggestion resulted in the synthesis of new sensitizers being the first members of the Antioxidant Carrier Sensitzer (ACS) group of molecules. According to preliminary experiments the PDT treatment of tumor bearing mice seems promising with two of such compounds which demonstrate an inhibitory effect in chemical systems already in their ground state.