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The aim is to evaluate how molecular descriptors like LogP and LogS influence the LogKoc of substituted diphenyl ethers.

April 3, 2026Chemical PapersOpen Access

Multivariate analysis about the influence of solubility and partition molecular descriptors on LogKoc for substituted diphenyl ethers

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The aim is to evaluate how molecular descriptors like LogP and LogS influence the LogKoc of substituted diphenyl ethers.
Analyzed a dataset of 59 substituted diphenyl ethers with experimental LogKow values.
Utilized 11 LogP and 4 LogS algorithms via univariate regressions with rigorous validation.
Developed multivariate models using partial least squares (PLS) regression assessed with cross-validation tests.
Investigated the hybrid descriptor LogSp for improved predictive modeling.
MLogP algorithm met all statistical criteria for model performance.
Different combinations of LogP and LogS algorithms significantly affected LogKoc prediction quality.
The hybrid LogSp descriptor produced the best QSPR model for internal and external validation.

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Multivariate analysis reveals the impact of molecular descriptors on LogKoc in substituted diphenyl ethers, suggesting improved prediction methods.

Silva et al. (2026) studied this question.

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Multivariate analysis reveals the impact of molecular descriptors on LogKoc in substituted diphenyl ethers, suggesting improved prediction methods.