Journal
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
Volume 40, Issue 4, Pages 1046-1051Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ci990131n
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QSPR models for logP and vapor pressures of organic compounds based on neural net interpretation of descriptors derived from quantum mechanical (semiempirical MO; AM1) calculations are presented. The models are cross-validated by dividing the compound set into several equal portions and training several individual multilayer feedforward neural nets (trained by the back-propagation of errors algorithm), each with a different portion as test set. The results of these nets are combined to give a mean predicted property value and a standard deviation. The performance of two models, for logP and the vapor pressure at room temperature, is analyzed, and the reliability of the predictions is tested.
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