Accurate & simple open-sourced no-code machine learning and CDFT predictive models for the antioxidant activity of phenols

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.