Louis, Bruno published the artcileQSAR modeling of aquatic toxicity of aromatic aldehydes using artificial neural network (ANN) and artificial neural network (MLR), Computed Properties of 286474-59-7, the main research area is QSAR aquatic toxicity aromatic aldehyde; artificial neural network artificial neural network.
In the present work, quant. structure-activity relationship anal. (QSAR) to predict the toxic potency of 77 aromatic aldehydes to ciliate Tetrahymena pyriformis has been investigated by means of multiple linear regression (MLR) and artificial neural network (ANN). The relationships between structure and toxicity were examined quant. using octanol/water partition coefficient (log Kow) encoding hydrophobic and mol. connectivity index depicting topol. structural features of aldehydes. The data set was split into train and test set and these sets were used to derive statistically robust and predictive (both internally and externally) models. The study demonstrates that both MLR and ANN models have good predictive power but ANN model shows a better statistical parameter in comparison with MLR model.
Journal of the Indian Chemical Society published new progress about Aquatic toxicity. 286474-59-7 belongs to class chlorides-buliding-blocks, name is 6-Chloro-2-fluoro-3-methylbenzaldehyde, and the molecular formula is C8H6ClFO, Computed Properties of 286474-59-7.
Referemce:
Chloride – Wikipedia,
Chlorides – an overview | ScienceDirect Topics