D'Anna, L.; Perricone, U.; De Simone, G.; MONARI, A.; Barone, G.; Terenzi, A.

AGAPE (computAtional G-quadruplex Affinitiy PrEdiction) is an innovative machine learning (ML)-based workflow developed to predict the potential stabilization of small molecules for G-quadruplex (G4) structures. G4s, especially prevalent in telomeres and oncogene promoters, represent promising therapeutic targets, but designing selective binders remains challenging. This study addresses this gap by implementing an ML framework in KNIME, specifically designed for ease of use across the scientific community. The AGAPE workflow integrates 5666 classical and quantum chemical (QC) descriptors for G4 ligands, enabling a comprehensive representation of binding-relevant molecular features. Using data from G4LDB and in-house collections, we created a robust dataset of 1217 compounds categorized by Forster Resonance Energy Transfer (FRET) assays as ACTIVE or INACTIVE based on G4 stabilization capacity. Feature selection algorithms and ML models, particularly XGBoost with Naive Bayes and Random Forest classifiers, were employed to achieve an optimized prediction model with an accuracy close to 90%. A consensus voting system among top-performing models further improved classification reliability. AGAPE efficiently predicts G4 stabilization, offers interpretability of key chemical interactions, and provides a scalable, accessible tool for G4-focused drug discovery. This workflow lays the foundation for targeted therapeutic development, enhancing ligand selectivity, and expanding the application of AI in chemoinformatics