ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

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

Natalya Aline Barros Ribeiro da Silva and Eduardo Borges de Melo

Department of Chemistry, Western Paraná State University, (UNIOESTE), Toledo, Brazil

E-mail: eduardo.b.de.melo@gmail.com

Received: 9 December 2025  Accepted: 9 March 2026

Abstract:

Substituted diphenyl ethers are compounds used in pesticides, pharmaceuticals, cosmetics, and other industrial applications. They exhibit high chemical stability and environmental persistence, raising concerns about toxicity and environmental fate. The soil organic carbon–water partition coefficient (LogK_oc) is a key parameter for evaluating this property. It is commonly modeled using descriptors related to hydrophobicity (LogP) and aqueous solubility (LogS). The predictive performance of such models may depend strongly on the computational algorithms used to derive these descriptors. In this study, a dataset comprising 59 substituted diphenyl ethers with previously determined experimental LogK_ow values was evaluated using 11 LogP and 4 LogS algorithms via univariate regressions, with rigorous validation criteria applied. Only the MLogP algorithm satisfied all statistical criteria. Subsequently, multivariate models based on partial least squares (PLS) regression were developed and assessed using LOO and LNO cross-validation, y-randomization tests, and applicability domain analysis. In addition, the hybrid descriptor LogS_p was investigated both in the original formulation and in a recalculated version derived from algorithms optimized for the present dataset. Consistent with prior studies, the results indicate that different combinations of LogP and LogS algorithms can directly affect the quality of LogK_oc predictive models, resulting in either good or poor performance. Moreover, the model incorporating the hybrid LogS_p descriptor, as originally proposed and now applied to a structurally homogeneous dataset, yielded the best QSPR model in terms of both internal and external quality, highlighting its potential for predicting this important parameter related to the toxicity and environmental fate of chemical compounds.

Keywords: Substituted diphenyl ethers; LogK oc; LogP; LogS; LogS p; QSPR

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-026-04795-5

Chemical Papers 80 (6) 6727–6744 (2026)