Prediction model based on decision tree analysis for laccase mediators

A Structure Activity Relationship (SAR) study for laccase mediator systems was performed in order to correctly classify different natural phenolic mediators. Decision tree (DT) classification models with a set of five quantum-chemical calculated molecular descriptors were used. These descriptors included redox potential (ɛ°), ionization energy (Ei), pKa, enthalpy of formation of radical (ΔfH), and OH bond dissociation energy (DO–H). The rationale for selecting these descriptors is derived from the laccase-mediator mechanism. To validate the DT predictions, the kinetic constants of different compounds as laccase substrates, their ability for pesticide transformation as laccase-mediators, and radical stability were experimentally determined using Coriolopsis gallica laccase and the pesticide dichlorophen. The prediction capability of the DT model based on three proposed descriptors showed a complete agreement with the obtained experimental results.

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► A Structure Activity Relationship study for laccase mediator systems was performed.
► Five quantum-chemical calculated molecular descriptors were used.
► Decision tree models were applied to predict the laccase-mediator performance.
► Mediator predictions were experimentally validated for pesticide transformation.
► Prediction model showed complete agreement with the obtained experimental results.