ArticleACS environmental Au2026
Prediction of Solute Descriptors for Linear Solvation Energy Relationships Using K‑Nearest Neighbors, Group Contributions, and Graph-Convolutional Neural Networks.
Article in ACS environmental Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Linear solvation energy relationship (LSER) models are nowadays often used to predict physicochemical properties of chemicals, such as partition coefficients, retention factors in chromatography, and solubilities. Due to their mechanistic foundation and transferability across phases, LSER models are particularly valuable for predicting partition coefficients in data-poor systems where experimental data sets are not available. However, their broader applicability is currently constrained by the limited availability of experimentally determined solute descriptors. We developed three different models, based on three different approaches, to predict the solute descriptors
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