Flavonoids as emerging therapeutic agents in leukemia based on conceptual frameworks computational analysis and future research direction

Flavonoids, a class of plant-derived natural products, are increasingly recognized as promising scaffolds in anticancer drug discovery due to their multi-target activities, favorable safety profiles, and structural adaptability. Yet, despite substantial progress in flavonoid-based research for solid tumors such as breast, lung, and colorectal cancers, their potential in leukemia therapy remains significantly underexplored, creating a critical gap in natural-product-driven hematologic drug discovery. This Perspective addresses that gap by illustrating, using daidzein as a representative example, how computational workflows encompassing density functional theory (DFT), molecular docking, and electrostatic surface mapping can uncover molecular determinants of stability, pharmacophoric hotspots, and interaction patterns with key leukemia-associated targets such as FLT3 and LSD1. While daidzein serves as an initial case study, the broader goal is to demonstrate how flavonoid-inspired chemotypes, across multiple subclasses, may be rationally optimized through in-silico analyses. Importantly, this work highlights a unique contribution: it proposes a conceptual roadmap for integrating computational chemistry with natural-product drug design at a stage where experimental high-throughput screening is costly, time-consuming, and often inaccessible for many research groups. In-silico pipelines enable rapid prioritization of candidate molecules, prediction of binding modes, and identification of key structural motifs, such as π–π stacking cores or hydrogen-bond donors/acceptors, that can guide synthetic modification and enhance translational potential. By framing flavonoids as a largely untapped reservoir for anti-leukemic discovery, we emphasize the urgent need for systematic computational exploration of this chemical space. We conclude by outlining emerging directions, including multi-flavonoid comparative analyses, AI-assisted scaffold optimization, and hybrid computational–experimental validation frameworks. These forward-looking approaches have the potential to accelerate the identification of next-generation, natural-product-based therapeutic leads for leukemia and ultimately contribute to improved patient outcomes and global health.