Purpose: This study aims to evaluate the potential of meciadanol as an anticancer drug candidate through an in silico approach in medicinal chemistry, focusing on its interaction with the DAPK1 (Death-Associated Protein Kinase 1) target protein and its pharmacokinetic feasibility.

Research Methodology: This research employed a computational approach using molecular docking analysis with AutoDock Vina to evaluate ligand-protein interactions between meciadanol and DAPK1. Furthermore, ADMET prediction was conducted using SwissADME and pkCSM to assess absorption, distribution, metabolism, excretion, and toxicity profiles of the compound.

Results: The molecular docking results demonstrated that meciadanol showed a strong binding affinity toward DAPK1 with a binding energy value of -8.2 kcal/mol, indicating stable molecular interactions through hydrogen bonds and van der Waals forces. ADMET analysis revealed that meciadanol has favorable pharmacokinetic characteristics, good oral bioavailability potential, and no predicted hepatotoxic, mutagenic, or carcinogenic effects.

Conclusions: The findings indicate that meciadanol has promising potential as an anticancer candidate and that in silico methods provide an efficient strategy for early-stage drug discovery by reducing time and cost requirements.

Limitations: This study is limited to computational predictions; therefore, further validation through in vitro and in vivo experiments is required to confirm biological effectiveness and safety.

Contribution: This research contributes to the development of computational-based medicinal chemistry approaches by providing preliminary evidence for meciadanol development as a structure-based anticancer agent.