Abstract

Phenolic compounds (PC) are important antioxidant biomolecules for medicine and foods industries. The DDPH test is used for testing antioxidant capacity. A fully No-Code methodology is presented for building QSPR models for the anti-DPPH activity of 202 PC. Machine learning (ML) algorithms were used for dimensionality reduction (PCA, InfoGain, GainRatio, CfsSubset) and predictive model training (J48, RandomTree, JCHAID*). Conceptual Density Functional Theory (CDFT) descriptors are calculated at the GFN1-xTB and GFN2-xTB levels of theory and the resulting global reactivity descriptors are used to train the ML models. The obtained Decision Tree (DT) models all present over 85% accuracy and Substantial Agreement with Reality, both for the internal and external validation. All the developed models adhere to the OECD guidelines for regulatory QSPR developments and are discussed in a mechanistic context. This research presents a novel, simple and codeless methodology for developing highly precise predictive models for the anti-DPPH activity of PC, successfully bridging the gap between experimental chemistry, theoretical physical chemistry, and ML.