Computational Evaluation Of Phytochemicals In Multi-Targeted Therapy Against AML And CML: A Molecular Docking And Drug-Likeness Study (Advances in Applied Biological Research, 2026)
Background and Rationale: Acute Myeloid Leukemia (AML) and Chronic  Myeloid Leukemia (CML) are different blood cancers. They face critical treatment  challenges, mainly drug resistance, such as the BCR-ABL1 T315I mutation, and the  presence of Leukemic Stem Cells (LSCs). In India, CML occurs at a much younger  age, creating an urgent need for cost-effective and safe alternatives to standard  Tyrosine Kinase Inhibitors (TKIs). Research Aim: This study aims to use detailed  computational (in-silico) methods to find and assess effective phytochemicals  that can serve as multi-target inhibitors. The main goal is to discover natural  compounds that can bypass known resistance mechanisms in CML and AML while  effectively targeting pathways crucial for LSC survival, such as PP2A and HIF-1.  Methodology: The research uses a high-throughput virtual screening strategy.  Molecular docking simulations evaluate the binding strength and suitability of  phytochemical libraries against key resistance targets (BCR-ABL1 T315I, FLT3) and  LSC regulators. At the same time, we conduct ADMET (Absorption, Distribution,  Metabolism, Excretion, and Toxicity) profiling and drug-likeness assessment  (Lipinski’s Rule of Five) to prioritize compounds with good oral bioavailability and  safety profiles suitable for long-term treatment. Conclusion: This study applies  polypharmacology to demonstrate how phytochemicals can function as effective  multi-target therapeutic leads. By moving beyond the traditional “one-drug-one target” approach, these natural bioactive compounds provide a strategic way to  bypass drug resistance and eliminate the resilient leukemic stem cell populations  that drive relapse in AML and CML.
Keywords: AML (Acute Myeloid Leukemia), CML (Chronic Myeloid Leukemia), phytochemicals, molecular docking, drug-likeness, ADMET, multi-target therapy, computational pharmacology.