Machine Learning-Driven QSAR Modeling of Anticancer Activity from a Rationally Designed Synthetic Flavone Library.

Flavones, recognized as "privileged scaffolds" in drug discovery, hold significant promise as anticancer agents. This study develops a quantitative structure-activity relationship (QSAR) model to accelerate the optimization of lead compounds. Using pharmacophore modeling against different cancer targets, 89 flavone analogs with varied substitution patterns were designed and synthesized. Biological evaluation revealed promising candidates with enhanced cytotoxicity against breast cancer (MCF-7) and liver cancer (HepG2) cell lines, along with low toxicity toward normal Vero cells. A machine learning (ML)-driven QSAR approach was employed, comparing random forest (RF), extreme gradient boosting, and artificial neural network (ANN) models. The RF model exhibits superior performance, achieving R2 of 0.820 for (MCF-7 and 0.835 for HepG2, with cross-validation (R2cv) of 0.744 and 0.770, respectively. Validation using 27 test compounds yielded root mean square error test values of 0.573 (MCF-7) and 0.563 (HepG2). SHapley Additive exPlanations analysis highlighted key molecular descriptors influencing anticancer activity. This work presents a robust ML-driven QSAR model that supports the rational design of flavone derivatives and advances the development of selective, potent anticancer agents.