Multivariate QSAR modelling of the rate of reductive dehalogenation of haloalkanes

Abstract

The pseudo first-order rate constants for reductive dehalogenation under anoxic conditions have recently been reported for a series of halogenated aliphatic hydrocarbons. in this paper it is shown that multivariate quantitative stnicture-activity relationship (QSAR) modelling of these data i s posible. Based on a training set of nine compounds and using hformation from 36 chemical and biological model systems, a QSAR was developed explaining 82% (R') and predicting 53% (e:,) of the variation in reductive dehalogenation. The QSAR was calculated using the partial least squares (PLS) method. The derived QSAR was validated using an e x t e d vaiidation set of six compounds for which expenmentally determined rate constants were available. The extemal validation showed that the QSAR could predict 62% ( Q2J of the response variation in the validation set. In addition, the validity of the QSAR was explored using a combination of cross-validation and permutation. Fifty repetitive randomizations of the real response were made and for each resulting reordered response the full PLS analysis was conducted. This yielded 50 pairs of Qi, and Qtt which were compared with those values of the 'real' model. Thus it was demonstrated that the QSAR produced Qkdues significantly exceedmg those of the random models and consequently the model was concluded to be valid. The interpretation of the QSAR suggested that the mechanism of reductive dehalogenation involves elements of hydrophobicity and electron transfer processes. O 1996 by John Wiley & Sons, Ltd.

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Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

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