Design, quantitative structure–activity relationships and computational studies of acylshikonin derivatives: Insights into antitumor activity

Cancer remains one of the leading global health challenges, and despite advances in therapy, current treatments are limited by toxicity, drug resistance, and lack of selectivity. Natural products, particularly shikonin and its derivatives, represent valuable scaffolds for anticancer drug discovery due to their diverse biological activities. However, the systematic identification of structural modifications that optimize pharmacological profiles is still lacking, highlighting the need for predictive computational approaches.To address this gap, we implemented an integrated in silico framework to evaluate 24 acylshikonin derivatives. Molecular descriptors were calculated and reduced via principal component analysis, followed by quantitative structure–activity relationship (QSAR) modeling using partial least squares, principal component regression, and multiple linear regression. In parallel, molecular docking was performed against the cancer-associated target 4ZAU, and ADMET/drug-likeness assessments were conducted to evaluate pharmacokinetic properties and synthetic accessibility. The PCR model demonstrated the highest predictive performance (R² = 0.912, RMSE = 0.119), emphasizing the importance of electronic and hydrophobic descriptors in cytotoxic activity. Docking simulations identified compound D1 as the most promising derivative, forming multiple stabilizing hydrogen bonds and hydrophobic interactions with key residues of 4ZAU. All derivatives satisfied major drug-likeness filters and exhibited acceptable synthetic accessibility, indicating favorable pharmacokinetic potential.This study advances the state of the art by demonstrating how an integrated QSAR–docking–ADMET workflow can rationalize the structure–activity relationship of shikonin derivatives, prioritize lead candidates, and accelerate their translation into anticancer drug discovery pipelines. The results highlight compound D1 as a promising scaffold for further optimization and encourage future validation through molecular dynamics simulations and experimental assays.