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Polychlorinated dibenzofurans (PCDFs) are industrial compounds or byproducts that are widely distributed in the environment.They cause toxic effects after binding to an intracellular cytosolic receptor called the aryl hydrocarbon receptor (AhR).Thymic atrophy,weight loss, immunotoxicity, acute lethality, and induction of cytochrome P4501A1 have all been correlated with the binding affinity of PCDFs to AhR.The hologram quantitative structure-activity relationship (HQSAR) as with other fragment-based methods, uses fragment fingerprints as a predictive variable of the biological activity variation or other related data.This method uses only 2D structure information, thus avoiding the usual conformational flexibility and structure alignments problems.In the present work, the HQSAR approach was performed on a set of structurally diverse PCDFs with known binding affinity.A HQSAR model with non-cross-validated regression coefficient of 0.913 and cross-validated regression coefficient of 0.736, was developed after optimizing the fragment size and the fragment distinction.The model was used to predict the binding affinities of the test set compounds, and agreement between the experimental and predicted values was verified, exhibiting a powerful predictive capability.These statistical parameters of obtained model suggest that the model is robust and seems to be as applicable as more complex methods, and the method can serve as a tool to introduce those who are planning to deal with computational toxicology.