Comment on “Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models” by N. Sizochenko, A. Gajewicz, J. Leszczynski and T. Puzyn, Nanoscale , 2016, 8, 7203
Source abstract
Abstract
In this comment we show that the accuracy of a recent nano-QSAR model for toxicity predictions of metal oxide nanoparticles towards bacteria E. coli can be greatly improved. On one hand, the experimental ionization energies of metal atoms could be substituted for the erroneous semi-empirically derived heat of formation values of metal ions as descriptors to construct a more reliable nano-QSAR model based on weighted linear least-squares fittings. On the other hand, if no experimental data is available, a model relying on ionization energy descriptors from quantum chemical calculations could also be used producing exactly the same toxicity values as the experimental model.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Computational Drug Discovery Methods · Geochemistry and Geologic Mapping
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physical Chemistry and Materials Science
University of Szeged
H-6720 Szeged
Hungary
Budapest University of Technology and Economics
H-1521 Budapest
Department of Applied and Environmental Chemistry
References (16)
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