QM/NN QSPR models with error estimation: Vapor pressure and LogP
Beck B, Breindl A, Clark T (2000)
Publication Status: Published
Publication Type: Journal article
Publication year: 2000
Journal
Book Volume: 40
Pages Range: 1046-1051
Journal Issue: 4
DOI: 10.1021/ci990131n
Abstract
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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Germany (DE)How to cite
APA:
Beck, B., Breindl, A., & Clark, T. (2000). QM/NN QSPR models with error estimation: Vapor pressure and LogP. Journal of Chemical Information and Computer Sciences, 40(4), 1046-1051. https://dx.doi.org/10.1021/ci990131n
MLA:
Beck, Bernd, Andreas Breindl, and Timothy Clark. "QM/NN QSPR models with error estimation: Vapor pressure and LogP." Journal of Chemical Information and Computer Sciences 40.4 (2000): 1046-1051.
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