Characterization of Mixtures Part 1: Prediction of Infinite-Dilution Activity Coefficients Using Neural Network-Based QSPR Models

The major problem in building QSAR/QSPR models for mixtures lies in their characterization. It has been shown that it is possible to construct QSPR models for the density of binary liquid mixtures using simple mole fraction weighted physicochemical descriptors. Such parameters are unsatisfactory; ho...

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Bibliographic Details
Published in:QSAR & combinatorial science 2008-12, Vol.27 (11-12), p.1346-1361
Main Authors: Ajmani, Subhash, Rogers, Stephen C., Barley, Mark H., Burgess, Andrew N., Livingstone, David J.
Format: Article
Language:English
Subjects:
dual-response model
Ensemble Neural Network
Mixtures
Neural Network
QSPR
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Summary:The major problem in building QSAR/QSPR models for mixtures lies in their characterization. It has been shown that it is possible to construct QSPR models for the density of binary liquid mixtures using simple mole fraction weighted physicochemical descriptors. Such parameters are unsatisfactory; however, from the point of view of interpretation of the resultant models. In this paper, an alternative mechanism‐based approach to the characterization of mixtures has been investigated. It has been shown that while it is not possible to build significant linear models using these descriptors, it has been possible to construct satisfactory artificial neural network models. The performance of these models and the importance of the individual descriptors are discussed.
ISSN:1611-020X
1611-0218
DOI:10.1002/qsar.200860022