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.
Highlights
Comment on “Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models” by N
Domonkos Attila Tasi, *a József Csontos,b Balázs Nagy, c Zoltán Kónya c and Gyula Tasi c. 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
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
Summary
Comment on “Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models” by N. 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.
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