Prediction of Henry's Law Constants by a Quantitative Structure Property Relationship and Neural Networks

@article{English2001PredictionOH,
  title={Prediction of Henry's Law Constants by a Quantitative Structure Property Relationship and Neural Networks},
  author={Niall J. English and Daniel G. Carroll},
  journal={Journal of chemical information and computer sciences},
  year={2001},
  volume={41 5},
  pages={1150-61}
}
Multiple linear regression analysis and neural networks were employed to develop predictive models for Henry's law constants (HLCs) for organic compounds of environmental concern in pure water at 25 degrees C, using a set of quantitative structure property relationship (QSPR)-based descriptors to encode various molecular structural features. Two estimation models were developed from a set of 303 compounds using 10 and 12 descriptors, one of these models using two descriptors to account for… CONTINUE READING
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